{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe the purpose of the data set you selected:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "I used the diabetes data set from UCI machine learning repository.  It has 101,766 patient records, with 55 attributes.  The data was collected from 130 hospitals between the years 1999 to 2008 for regular, non-intensive-care-unit hospital admissions.  The records reflect the clinical data collected during a patient's hospital stay, pertinent to the treatment of diabetes.  (Since my wife has diabetes, I was interested in the type of data collected).\n",
    "\n",
    "The purpose of the data set was to determine which characteristics of the patient and care they received had the most bearing on long-term health, which was determined by whether or not the patient was readmitted to the hospital within 30 days or after 30 days.\n",
    "\n",
    "A good prediction algorithm would establish a relationship or correlation between a specific attribute or group of attributes with the probability of readmission to the hospital.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding - Describe the meaning and type of data for each attribute in the data file"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The description of all 55 fields of the data attributes can be found at http://www.hindawi.com/journals/bmri/2014/781670/tab1/.  I only used 10 of the 55 attributes because on this assignment because I was learning to use the tools, and found  visualizing and using 55 fields very cumbersome.  Some of the fields, such as patient number and encounter ID, provided no useful information.  A few of the fields, such as weight, had so many missing entries that they were useless.  \n",
    "\n",
    "I have included the description of the fields that I used below \n",
    "\n",
    "I have included this table here, with some comments:\n",
    "Attribute           Data Type    Description                        %missing      \n",
    "Race\t            Nominal\t     Caucasian, Asian, \n",
    "                                 African American, Hispanic, \n",
    "                                 and other\t                        2%\n",
    "Gender              Nominal\t     male, female, and unknown/invalid\t0%\n",
    "Age\t                Nominal      Grouped in 10-year intervals       0%\n",
    "Time in hospital    Integer      Number of days between admission \n",
    "                                 and discharge                      0%\n",
    "Glucose serum test result\n",
    "                    Nominal      Indicates the range of the result\n",
    "                                 or if the test was not taken. \n",
    "                                 Values: “>200,” “>300,” “normal,”\n",
    "                                 and “none” if not measured         0%            \n",
    "A1c test result     Nominal      Indicates the range of the \n",
    "                                 result or if the test was not \n",
    "                                 taken. Values: “>8” if the result\n",
    "                                 was greater than 8%, “>7” if the \n",
    "                                 result was greater than 7% but \n",
    "                                 less than 8%, “normal” if the \n",
    "                                 result was less than 7%, and \n",
    "                                 “none” if not measured.           0%\n",
    "Change of medications\t\n",
    "                   Nominal       Indicates if there was a change\n",
    "                                 in diabetic medications (either \n",
    "                                 dosage or generic name). \n",
    "                                 Values: “change” and “no change”\t0%\n",
    "Diabetes medications\n",
    "                   Nominal       Indicates if there was any \n",
    "                                 diabetic medication prescribed. \n",
    "                                 Values: “yes” and “no”             0%\n",
    "Readmitted\t       Nominal       Days to inpatient readmission. \n",
    "                                 Values: “<30” if the patient \n",
    "                                 was readmitted in less than 30 \n",
    "                                 days, “>30” if the patient was \n",
    "                                 readmitted in more than 30 days, \n",
    "                                 and “No” for no record of readmission.\t0%\n",
    "\n",
    "The glucose serum test is the standard pin-prick blood test that diabetics take on a daily basis.  A normal value taken two hours after eating is below 150 mg/dL.  Higher than 200 is considered poor, and higher than 300 is dangerous.  Prolonged high levels are dangerous to long-term health.\n",
    "\n",
    "The A1C test reflects average blood sugar values over a 60 to 90 day interval. A normal value is around 5.0.  Patients with diabetes are advised to keep it below 7.  Above 7 is considered relatively high.  Above 8 indicates that the patient's diabetes is not being well managed.\n",
    "\n",
    "Although the data description provided indicates that there were no missing values for the Glucose Serum Test and A1C test result, there were actually only very few tests.  Most of the attributes indicated \"None\", indicating that no test was done.  There were 5346 measurements for the glucose serum test (out of 100,000 patients), and 17018 measurements of the A1C level.  \n",
    "\n",
    "I left out the diagnosis codes because they are nominal data taken from insurance company diagnosis codes to which I did not have access at the time I analyzed the data.\n",
    "\n",
    "I removed the unused fields with the LibrOffice Calc spreadsheet program rather than ipython.  On the spreadsheet the data columns were easier to see and remove than with ipython.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify Data Quality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, there were many missing values for the A1C and glucose serum test results. For these I used the mean value or median value of the existing measurements for some calculations.  For other calculations I used only the patients for which data were provided.  The values for \"race\" had many missing values also, and I replaced these values with \"Other\".\n",
    "\n",
    "The provided data description mentioned no duplicates.\n",
    "\n",
    "All of the meaurements were put into nominal ranges, and therefore there were no outliers.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 47664\r\n",
      "drwxr-xr-x@ 5 eclarson  staff       170 Feb  6  2015 \u001b[34m.\u001b[m\u001b[m\r\n",
      "drwxr-xr-x@ 6 eclarson  staff       204 Sep 10 10:35 \u001b[34m..\u001b[m\u001b[m\r\n",
      "-rwxr-xr-x@ 1 eclarson  staff      2547 May 15  2014 \u001b[31mIDs_mapping.csv\u001b[m\u001b[m\r\n",
      "-rwxr-xr-x@ 1 eclarson  staff  19159383 May 15  2014 \u001b[31mdiabetic_data.csv\u001b[m\u001b[m\r\n",
      "-rwxr-xr-x@ 1 eclarson  staff   5238567 Feb  2  2015 \u001b[31mdiabetic_data_trimmed.csv\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!ls -all Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Data/diabetic_data_trimmed.csv\") #read in the csv file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 101766 entries, 0 to 101765\n",
      "Data columns (total 13 columns):\n",
      "race                99493 non-null object\n",
      "gender              101766 non-null object\n",
      "age                 101766 non-null int64\n",
      "time_in_hospital    101766 non-null int64\n",
      "max_glu_serum       101766 non-null object\n",
      "A1Cresult           101766 non-null object\n",
      "insulin             101766 non-null int64\n",
      "change              101766 non-null int64\n",
      "diabetesMed         101766 non-null int64\n",
      "readmitted          101766 non-null int64\n",
      "serum               101766 non-null float64\n",
      "a1c                 101766 non-null float64\n",
      "sex                 101766 non-null float64\n",
      "dtypes: float64(3), int64(6), object(4)"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>insulin</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "      <th>serum</th>\n",
       "      <th>a1c</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "      <td> 101766.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>     65.967022</td>\n",
       "      <td>      4.395987</td>\n",
       "      <td>      1.420101</td>\n",
       "      <td>      0.461952</td>\n",
       "      <td>      0.770031</td>\n",
       "      <td>      0.460881</td>\n",
       "      <td>      1.750655</td>\n",
       "      <td>      7.507183</td>\n",
       "      <td>      0.537602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>     15.940838</td>\n",
       "      <td>      2.985108</td>\n",
       "      <td>      1.455199</td>\n",
       "      <td>      0.498553</td>\n",
       "      <td>      0.420815</td>\n",
       "      <td>      0.498470</td>\n",
       "      <td>      0.186210</td>\n",
       "      <td>      0.437048</td>\n",
       "      <td>      0.498579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>      5.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      6.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>     55.000000</td>\n",
       "      <td>      2.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      1.750655</td>\n",
       "      <td>      7.500000</td>\n",
       "      <td>      0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>     65.000000</td>\n",
       "      <td>      4.000000</td>\n",
       "      <td>      2.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      0.000000</td>\n",
       "      <td>      1.750655</td>\n",
       "      <td>      7.500000</td>\n",
       "      <td>      1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>     75.000000</td>\n",
       "      <td>      6.000000</td>\n",
       "      <td>      2.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      1.750655</td>\n",
       "      <td>      7.500000</td>\n",
       "      <td>      1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>     95.000000</td>\n",
       "      <td>     14.000000</td>\n",
       "      <td>      4.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      1.000000</td>\n",
       "      <td>      3.000000</td>\n",
       "      <td>      8.500000</td>\n",
       "      <td>      1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age  time_in_hospital        insulin         change  \\\n",
       "count  101766.000000     101766.000000  101766.000000  101766.000000   \n",
       "mean       65.967022          4.395987       1.420101       0.461952   \n",
       "std        15.940838          2.985108       1.455199       0.498553   \n",
       "min         5.000000          1.000000       0.000000       0.000000   \n",
       "25%        55.000000          2.000000       0.000000       0.000000   \n",
       "50%        65.000000          4.000000       2.000000       0.000000   \n",
       "75%        75.000000          6.000000       2.000000       1.000000   \n",
       "max        95.000000         14.000000       4.000000       1.000000   \n",
       "\n",
       "         diabetesMed     readmitted          serum            a1c  \\\n",
       "count  101766.000000  101766.000000  101766.000000  101766.000000   \n",
       "mean        0.770031       0.460881       1.750655       7.507183   \n",
       "std         0.420815       0.498470       0.186210       0.437048   \n",
       "min         0.000000       0.000000       1.000000       6.000000   \n",
       "25%         1.000000       0.000000       1.750655       7.500000   \n",
       "50%         1.000000       0.000000       1.750655       7.500000   \n",
       "75%         1.000000       1.000000       1.750655       7.500000   \n",
       "max         1.000000       1.000000       3.000000       8.500000   \n",
       "\n",
       "                 sex  \n",
       "count  101766.000000  \n",
       "mean        0.537602  \n",
       "std         0.498579  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from the instructor: note that there are more compact syntax representations for this\n",
    "# the \"replace\" function in pandas is very powerful\n",
    "# http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.replace.html \n",
    "\n",
    "import numpy as np\n",
    "import copy\n",
    "df = df.replace(to_replace='?',value=np.nan) # replace '?' with NaN (not a number)\n",
    "df = df.replace(to_replace=\"[0-10)\",value=5) # convert age ranges to numeric ordinal\n",
    "df = df.replace(to_replace=\"[10-20)\",value=15)\n",
    "df = df.replace(to_replace=\"[20-30)\",value=25)\n",
    "df = df.replace(to_replace=\"[30-40)\",value=35)\n",
    "df = df.replace(to_replace=\"[40-50)\",value=45)\n",
    "df = df.replace(to_replace=\"[50-60)\",value=55)\n",
    "df = df.replace(to_replace=\"[60-70)\",value=65)\n",
    "df = df.replace(to_replace=\"[70-80)\",value=75)\n",
    "df = df.replace(to_replace=\"[80-90)\",value=85)\n",
    "df = df.replace(to_replace=\"[90-100)\",value=95)\n",
    "df = df.replace(to_replace=\"[100-110)\",value=105)\n",
    "df = df.replace(to_replace='No',value=0) # convert boolean to numeric\n",
    "df = df.replace(to_replace=\"Yes\", value=1)\n",
    "df = df.replace(to_replace='Ch',value=1) #convert med change to numeric\n",
    "df = df.replace(to_replace='Steady',value=2)\n",
    "df = df.replace(to_replace='Up',value=3)\n",
    "df = df.replace(to_replace='Down',value=4)\n",
    "df = df.replace(to_replace='NO',value=0) #convert readmission (what we are trying to calculate to numeric\n",
    "df = df.replace(to_replace='<30',value=1)\n",
    "df = df.replace(to_replace='>30',value=1)\n",
    "\n",
    "#replace blood sugar measurement (max_glu_serum) with numeric values for analysis\n",
    "df_serum = copy.deepcopy(df.max_glu_serum)\n",
    "df_serum = df_serum.replace(to_replace='Norm',value=1.0) # normal is in the 80 to 130 range\n",
    "df_serum = df_serum.replace(to_replace='>200',value=2.0) # use 2 (instead of 200) as a nominal value here\n",
    "df_serum = df_serum.replace(to_replace='>300',value=3.0) # use 3 as a nominal value here\n",
    "df_serum = df_serum.replace(to_replace='None',value=np.nan) # use not a number as an unknown value here, so we can identify it\n",
    "#convert the nans to the mean value so the average doesn't change\n",
    "df_serum = df_serum.replace(to_replace=np.nan, value=df_serum.mean())\n",
    "df[\"serum\"] = df_serum\n",
    "\n",
    "# adjust the A1C measurements\n",
    "df_a1c = copy.deepcopy(df.A1Cresult)\n",
    "df_a1c = df_a1c.replace(to_replace='>7',value=7.5) # use 7.5 as a nominal value here\n",
    "df_a1c = df_a1c.replace(to_replace='>8',value=8.5) # use 8.5 as a nominal value here\n",
    "df_a1c = df_a1c.replace(to_replace='None', value=np.nan);\n",
    "df_a1c = df_a1c.replace(to_replace='Norm', value=6.0);\n",
    "# use the median for the \"nan\" values to keep the grouping for the bar charts\n",
    "df_a1c = df_a1c.replace(to_replace=np.nan, value=df_a1c.median())\n",
    "df[\"a1c\"] = df_a1c\n",
    "\n",
    "# make the sex numeric so we can see the percentage of males to females\n",
    "df_sex = copy.deepcopy(df.gender)\n",
    "df_sex = df_sex.replace(to_replace='Male',value=0) #convert sex to numeric\n",
    "df_sex = df_sex.replace(to_replace='Female',value=1)\n",
    "df_sex = df_sex.replace(to_replace='Unknown/Invalid',value=np.nan) # make unknown sex the average\n",
    "df_sex = df_sex.replace(to_replace=np.nan, value=df_sex.mean())\n",
    "df[\"sex\"] = df_sex\n",
    "\n",
    "df.info()\n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above shows the simple statistics for the numeric values in the table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the average patient was between 60 and 70 years old, spent slightly over 4 days in the hospital, was likely to have had no change in medication, was probably taking some type of diabetes medication, had a glucose serum level of about 175, had an A1C level of 7.5, and was slightly more likely to be female than male.\n",
    "\n",
    "This matches the provided data description in that one would expect a higher than normal glucose serum level and A1C test result in a population of diabetics. A patient also had a 46% chance of being readmitted to the hospital. \n",
    "\n",
    "The counts show that all of the missing data have been replaced with the mean values of the provided data.  I also calculated some cross-field statistics as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#import the other packages\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter('ignore', DeprecationWarning)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.088084429\n"
     ]
    }
   ],
   "source": [
    "# find the percentage of people who were readmitted\n",
    "percentReadmitted = float(len(df[df.readmitted != 0]))/len(df) * 100\n",
    "print percentReadmitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The readmission percentage above matches the calculation from the data frame describe() function in the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.3141688022\n"
     ]
    }
   ],
   "source": [
    "# find the percentage of people who were readmitted if they had an A1C measurement greater than normal (6.0)\n",
    "df_A1C = copy.deepcopy(df[df.a1c > 6.0])\n",
    "a1cLen = len(df_A1C);\n",
    "df_A1C = df_A1C[df_A1C.readmitted > 0]\n",
    "percentBoth = float(len(df_A1C))/float(a1cLen) * 100\n",
    "print percentBoth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This number is the percentage of patients who were readmitted if they had an elevated A1C measurement.  It shows that a person with an elevated A1C measurement was only slightly more likely to be readmitted to the hospital than the average patient. (46.31% vs 46.09%) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.1333287649\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of people who were readmitted if they stayed in the hospital more than least one day\n",
    "df_hospitalized = copy.deepcopy(df[df.time_in_hospital > 1.0])\n",
    "hospLen = len(df_hospitalized)\n",
    "df_hospitalized = df_hospitalized[df_hospitalized.readmitted > 0]\n",
    "percentBoth = float(len(df_hospitalized))/float(hospLen) * 100\n",
    "print percentBoth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see the percentage of patients eventually readmitted who had an initial hospital stay of more than one day.  A person with a longer hospital stay was slightly more likely to be readmitted than the average patient (47.13% vs 46.09%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48.1713035324\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of people who were readmitted if they had an insulin change\n",
    "df_change = copy.deepcopy(df[df.insulin > 1])\n",
    "changeLen = len(df_change)\n",
    "df_change = df_change[df_change.readmitted > 0]\n",
    "percentBoth = float(len(df_change))/float(changeLen) * 100\n",
    "print percentBoth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we we see the percentage of patients eventually readmitted after their insulin dosage was changed.  |A person with an insulin change was about 2% more likely to be readmitted than the average patient (48.17% vs 46.09%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43.9299565166\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of people who were readmitted if they had an A1C test at all\n",
    "df_a1ctest = copy.deepcopy(df[df.A1Cresult != 'None'])\n",
    "a1cLen = len(df_a1ctest)\n",
    "df_a1ctest = df_a1ctest[df_a1ctest.readmitted > 0]\n",
    "percentBoth = float(len(df_a1ctest))/float(a1cLen) * 100\n",
    "print percentBoth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the hospital administered an A1C test on a patient, the patient was about 2% LESS likely to be readmitted than the average patient (43.92% vs 46.09%).  This is regardless of the test result (normal or elevated).  This is counterintuitive in that the act of running the test affected the result, not the measurement itself.  \n",
    "\n",
    "One possible explanation for this (and I am guessing here) is that patients that had the test with an elevated result and were informed of the result may have taken better care of themselves after their first hospital stay.\n",
    "\n",
    "Some cross tabulations of a few of the fields is enlightening\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1140b6bd0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P2W45MDSkL8tJXxHSqyJGv809/8bqYzzO7vk3Vt+eItsY20hNT/hKOhp42czm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f82090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5oI31aVI0P2FX1QXc518C3Ty1i6ZbGOPvOI7jtJ9C+PwldeoshLt9ugPu83ec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\nIibQfwDhP2Xr4yDdFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f82d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readmission_counts = pd.crosstab([df['gender'],df['age']], df.readmitted.astype(bool))\n",
    "readmission_counts.plot(kind='bar', stacked=True, color=['green','red'])\n",
    "\n",
    "readmission_counts = pd.crosstab([df['race'],df['gender']], df.readmitted.astype(bool))\n",
    "readmission_counts.plot(kind='bar', stacked=True, color=['green','red'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above that the majority of patients were in the 65 to 80 age group, with very few very young patients.\n",
    "Also, the majority were Caucasian with some African Americans.  There were comparatively few Hispanic and Asian patients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization and Attribute Relationships"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following section shows bar charts and cross tabulations of the relationships between the different attributes and the readmission rates for each group of attributes.  The bar charts are very convenient in that they quickly show the differences between the readmission rates for each grouped attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0xada9820c>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DWVtbQy6X4/z589Iybeu0V69eePXVV6Vl2uVhYWEYN24c+vTpg7Zt20rLH90mPDwciYmJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xada98d2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print a bar chart of the people who were readmitted by A1Cresult\n",
    "df_a1c = copy.deepcopy(df)\n",
    "#replace the 2's with 1's so that the sum works out\n",
    "df_a1c.readmitted = df_a1c.readmitted.replace(to_replace=2,value=1)\n",
    "df_a1c = df_a1c.groupby(by=['gender','A1Cresult'])\n",
    "readmission_rate = df_a1c.readmitted.sum() / df_a1c.readmitted.count()\n",
    "readmission_rate.plot(kind='barh', stacked=True, color = ['green', 'red'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a bar chart graphing A1C levels and gender against the likelihood of readmission to the hospital.  This chart reflects the counterintuitive result above in that a male with no A1C measurement was more likely to be readmitted than a male with an elevated A1C measurement.  And a female with no measurement at all was just about as likely to be readmitted as a female with an elevated A1C measurement.  The bar chart was chosen because it allows easy comparison between groups.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0xadb0ad8c>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WX331lWJHRUWJwYMHC09PT7FgwQKh1+sb7LugoEDY29sLb29v4e3tLebNmydK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\nmRBCCPEANCATQgghHoAGZEIIIcQD/D+ifqInkh2jdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xadb0a30c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print a bar chart of the people who were readmitted by age range and gender\n",
    "df_ageGroup = copy.deepcopy(df)\n",
    "#replace the 2's with 1's so that the sum works out\n",
    "df_ageGroup.readmitted = df_ageGroup.readmitted.replace(to_replace=2,value=1)\n",
    "df_ageGroup = df_ageGroup.groupby(by=['age','gender'])\n",
    "readmission_rate = df_ageGroup.readmitted.sum() / df_ageGroup.readmitted.count()\n",
    "readmission_rate.plot(kind='barh', stacked=True, color = ['green', 'red'])\n",
    "                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar chart of age and gender against the likelihood of readmission.\n",
    "\n",
    "We can see here that the younger patients were, in general, less likely to be readmitted to the hospital than the older patients (with the exception of female patients in the 20 to 20 age group).  This would follow if older patients are in more fragile health and less resilient than younger patients. It is also interesting that patients older than 90 were less likely to be readmitted than the patients over 25.  This may be due to the fact that some of the very old patients passed away before they could be readmitted.  Again, the bar chart allows for easy comparison - the Female patients in the 15 year (10 to 20) and 25 year (20 to 30) were somewhat more likely to be readmitted than their male counterparts, but I have no explanation for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0xaef48eac>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+OGHGDFiBJ5++mkkJSWprSOTyZq+nnWQuLg4uLm5AQDs7e0REBCA8PBwAEBe\nXh4ACNPKJ8fQ00q65PPy8nTevtaP9+92hivb23y6vccHiE+3k0cRgAsABqlMi+SV21BO5TVrrzLD\nr69pH6+wPppeo3CVaU3uT3Dh738H/TNL7PUF0LQPKddvltf1/dBee6Wwf2RnZ2Pjxo0AIPSXbdGp\n5n/hwgWEhISgqKjpXfzTTz9h1apVOHv2LPbs2QMnJyeUlZUhIiICJ0+eFP4wLF26FAAQGRmJ5cuX\nw9XVFREREThx4gQAIDU1FXv37sVHH32k3kiu+euMa/7MULjm3/l1eM3fyckJAwcOREFBAQBg586d\nGDJkCCZPnoyUlBQAQEpKCqZOnQoAmDJlCtLS0lBXV4eioiIUFhYiODgYTk5OsLOzQ25uLogImzZt\nEjKMMcYMR+ehnh988AFmzZoFf39//Pbbb/j3v/+NpUuXYseOHfDw8MDu3buFT/o+Pj6YPn06fHx8\nEBUVheTkZKEklJycjLlz50Iul8Pd3b3FSB9N6FMDs7isFus2H1LX/KbVsDquy5p3VuckuOZvxKwq\nnc/q6e/vj19//bXF/J07d7a6fmJiIhITE1vMDwoKwrFjx3RtBjOgW4D612vVOiuAW6rLGGOSwuf2\nMXOmrNtzzd+8cc2/8+Nz+zDGGFNjFp2/FGtvFleb5bqseWd1ToJr/kbMqjKLzp8xxph2uOZv5rjm\nzwyFa/6dH1/Dl0mKcoip2HLGmH7MouwjxdqbxdVmtcgKQ0yVt1j16VtabFaSr5EUszonwTV/I2ZV\nmUXnzxhjTDtc8zdzUq3565plxsM1/86Px/kzxhhTYxadvxRrbxZXmzXVMQJSfI0kkhU795Oxzvuk\nbz5bj81K5XVqC4/2YYzpRO3cT3zeJ8nhmr+Z45o/MxRT1u255q8ZrvkzxlrV185OuOpe81tfOztT\nN48ZkFl0/lKsvWmTFXuDavsm1XyrreCav9llK6urQQAIwJ6//1XeKqurNb8jU+0beuaz9disVF7j\ntphF52/uVN+ger9JGWMMXPOXBKleh5dr/p2fVMfqc81fM3xuHwvG58lhjLXGLMo+Uqy9GWusfkee\nJ4dr/mae1TkJrvlLJKvKLDp/xhhj2uGavwRYWt2ea/7GwzV/lWUwv/2Kx/kzxhhTYxadvxRrbxZX\nm+Waf+fP6pwE1/wlklWlV+ff0NCAwMBATJ48GQBQUVEBhUIBDw8P3HPPPbhy5Yqw7qpVqyCXy+Hl\n5YWsrCzcWvXkAAAgAElEQVRh/qFDh+Dn5we5XI5Fixbp0xzGGGMa0qvzX7NmDXx8fJpqcACSkpKg\nUChQUFCA8ePHIykpCQCQn5+PLVu2ID8/H5mZmZg3b55Qh0pISMD69etRWFiIwsJCZGZmat2O8PBw\nnR+DJLM6J6F28i1LyEry9eX9ymj5cD02K8XXWJXOnf/58+exfft2zJ07V+jIMzIyEBsbCwCIjY3F\ntm3bAADp6emYOXMmrK2t4ebmBnd3d+Tm5qKsrAzV1dUIDg4GAMyePVvIMMYYMxydO/9nnnkGb775\nJqys/rmLixcvwtHREQDg6OiIixcvAgBKS0vh4uIirOfi4oKSkpIW852dnVFSUqJ1W6RYe7O42izX\n/Dt/VuckuOYvkawqnY7w/fbbb3HHHXcgMDCwzYYoTzrWUeLi4uDm5gYAsLe3R0BAgPD1Jy8vD8A/\nX4eUbTL0tJIu+by8PI3XB5p2UuVU3t///rO0KdNmXvnmUH49bjbd3uNr8eZqNq1R/kIr228jL6yj\nXP9Cs/ZD/PFK7fVtPm30/RnqWkx38OvbIq/l6ytso439Q9PHq7y3Zq2R9P6RnZ2NjRs3AoDQX7ZF\np3H+iYmJ2LRpE7p27YqbN2+iqqoKDzzwAH799VdkZ2fDyckJZWVliIiIwMmTJ4Xa/9KlSwEAkZGR\nWL58OVxdXREREYETJ04AAFJTU7F371589NFH6o3kcf48zl/DLNMOj/NXWQbz2686fJz/66+/jj//\n/BNFRUVIS0vDuHHjsGnTJkyZMgUpKSkAgJSUFEydOhUAMGXKFKSlpaGurg5FRUUoLCxEcHAwnJyc\nYGdnh9zcXBARNm3aJGQYY4wZToeM81eWd5YuXYodO3bAw8MDu3fvFj7p+/j4YPr06fDx8UFUVBSS\nk5OFTHJyMubOnQu5XA53d3dERkZqvX1T1c9MltU5CUnW7bnmb6SszklwzV8iWVV6n9UzLCwMYWFh\nAIC+ffti586dra6XmJiIxMTEFvODgoJw7NgxfZvBGGNMC3xuHwngmr/mWaYdrvmrLIP57Vd8bh/G\nGGNqzKLzl2LtzeJqs1zz7/xZnZPgmr9Esqr4Sl6MMYshdmU7S7uqHdf8JYBr/ppnmXYsseava1aK\nuObPGGNMjVl0/lKsvVlcbZZr/p0/q3MSkq35W9q+pcosOn/GGGPa4Zq/BHDNX/Ms0w7X/DXPShHX\n/BljjKkxi85firU3i6vNcl2282d1ToJr/hLJqjKLzp8xxph2uOYvAVzz1zzLtMM1f82zUsQ1f8YY\nY2rMovOXYu3N4mqzXJft/Fmdk+Cav0SyqvjcPoxZMD7XjeXimr8EcM1f8yzTjhRr71zz15xY38mf\n/CVA7NOZcjljjGmDa/4SyN4Cmj6tKG+x6tO3tNmwBOv2llaXNVk9WIKvrym3LcnXWIVZdP6MMca0\nYxadf3h4uEVlMUj3qKVlpfj68n4ljW1L8jVWYRadP2OMMe2YRecvxdqbxdVmuS7b6bNSfH1NuW1J\nvsYqdOr8//zzT0RERGDIkCHw9fXF+++/DwCoqKiAQqGAh4cH7rnnHly5ckXIrFq1CnK5HF5eXsjK\nyhLmHzp0CH5+fpDL5Vi0aJGeD4cxxpgmdOr8ra2t8e677+L48ePIycnB2rVrceLECSQlJUGhUKCg\noADjx49HUlISACA/Px9btmxBfn4+MjMzMW/ePGHsaUJCAtavX4/CwkIUFhYiMzNT6/ZIofbW184O\nMplMuEVERKhN97Wz03zDUqzNcl2202el+PqactuSfI1V6NT5Ozk5ISAgAABgY2MDb29vlJSUICMj\nA7GxsQCA2NhYbNu2DQCQnp6OmTNnwtraGm5ubnB3d0dubi7KyspQXV2N4OBgAMDs2bOFjLmprK4G\nAW3eKqurTdg6xpil0bvmX1xcjCNHjmDkyJG4ePEiHB0dAQCOjo64ePEiAKC0tBQuLi5CxsXFBSUl\nJS3mOzs7o6SkROs2SLH2pnsS0qzNcl2202el+PqactuSfI1V6HWEb01NDR588EGsWbMGtra2asuU\n5YyOEhcXBzc3NwCAvb09AgIChK8/eXl5AP75OqR8cgw9raT1+gDyAISrTIvdH4CmnVT5FfXC3/8O\nUs+0tX1hB1eu32y6vfa2eIM0m9Yof6GV7beRF9bR4fH2tbMT/RbVx9YWX2VktNpefV9f1em8vDyd\n9y9j78/Gfn1b5LXdn5XbaGP/0PjxatleKewf2dnZ2LhxIwAI/WVbdD63z61btxAdHY2oqCg8/fTT\nAAAvLy9kZ2fDyckJZWVliIiIwMmTJ4Xa/9KlSwEAkZGRWL58OVxdXREREYETJ04AAFJTU7F37158\n9NFH6o00g3P78Pl5jJfV9Xm2RFI8xw6f20dzHX4+fyLCY489Bh8fH6HjB4ApU6YgJSUFAJCSkoKp\nU6cK89PS0lBXV4eioiIUFhYiODgYTk5OsLOzQ25uLogImzZtEjKMMcYMR6fOf//+/fjss8+wZ88e\nBAYGIjAwEJmZmVi6dCl27NgBDw8P7N69W/ik7+Pjg+nTp8PHxwdRUVFITk4WSkLJycmYO3cu5HI5\n3N3dERkZqXV7pFh70z0JadZmTVWX1WOzktyvuOZvtKwkX2MVOtX87777bjQ2Nra6bOfOna3OT0xM\nRGJiYov5QUFBOHbsmC7NYIwxpiOzOMJXiuNtdU9CmuOxTTUWW4/NSnK/4nH+RstK8jVWYRadP2OW\nrPkBhDofPMgsill0/lKsvemehDRrs1zzN1hW9QDCPdDj4EEJvr6m3LZU9o+2mEXnzxhjTDtm0flL\nsfamexLSrM1yzd84WZ2TkOTra8ptS3H/UGUWnT9jjDHtmEXnL4Xam/Ii7G3dtLoIuxRrs1zzN05W\n5yQk+fqacttS3D9U6XVuH6Y54SLsSqrnJgFwS3UZY4wZmFl0/pKsvUmxviqBrPIblthyTUlxv9I9\nCUm8vp1p21LcP1SZRedvLJqcMbKiqsqILWLNtfiG1Xy5yDLGLAnX/LXINr8gi8WNqbawrBRruron\nIcnXyJTbluL+ocosOn/GGGPaMYvOn+urnDVEVoo1Xd2TkORrZMptS3H/UMU1f8YkTuxHbq2GEDOL\nYhaf/Lm+yllDZKVS0xV+5F4GIFbl/8v+XqYpCb5Gxtx2R55Aj2v+jDEmEWIDPrQa7NFJmEXnz/VV\nzhoiK8margSfZ8nW/PXYLNf8JaYjDyBijDFTMotP/saqn6nVVpfB8uqrFpY1Zl22w+rJEnyepVLz\nby5bj812hpo/f/JnrBNQ1pOBpk4lXGWZTIL1ZHNkbqOqzKLz16YG1qGnaJBijZSzGuPfkiSQNeK2\nxU4dou1pQ7jmbwKqn7Baw5+yGGOWoFPU/DMzM+Hl5QW5XI7Vq1drneex+pw1RFab/UqsZq9J3V7s\neg98rYdOum2J/J7UFpN3/g0NDZg/fz4yMzORn5+P1NRUnDhxQqv7yMvL03jd5m+yCOjxRrugzcqc\nlVpWm/2q+Rjwd6HdSf/UBhNMhO4DCST4POuVNeW2jbRvdWRWlcnLPgcOHIC7uzvc3NwAAA8//DDS\n09Ph7e2t8X1cuXJF43Vb1O32oOkvgHK56rL23NRiXc5KLqvNftXaj4HPNFuuMQk+VybLmnLbRtq3\nOjKryuSdf0lJCQYOHChMu7i4IDc3VzTT2o+2y5cvB8Dn1Ge6E9uvAPF9q0M/VDBmBCYv+8hkYodN\nta5G5Cu02LJW6fNHlLNmlW1v39Fq35LA4zWLrCm3rUe2uLjYJFk1ZGK//PILTZw4UZh+/fXXKSkp\nSW0df39/1fIp3/jGN77xTYObv79/m32vjIgIJlRfXw9PT0/s2rULAwYMQHBwMFJTU7Wq+TPGGNOO\nyWv+Xbt2xYcffoiJEyeioaEBjz32GHf8jDFmYCb/5M8YY8z4TP6Dr6ncvHkTtbW1nOUsY0bTmfZp\nk5d9tHXr1i1kZWVh3759KC4uhkwmg6urK8aOHYuJEyeia9fWH1JjYyO2bduG1NRU/Pzzz2hsbAQR\noUuXLggJCcGsWbMwderUVkcfcda8s6pOnDiB4uJiWFlZwdXVFV5eXqLrA7rvk5zVLivFdneGfbot\nkir7rFixAlu3bkVISAiCg4MxYMAANDY2oqysDAcOHEBOTg4eeughvPjiiy2yY8eORWhoKKZMmYKA\ngAB0794dAFBbW4sjR44gIyMDP/30E/bt28dZC8sWFRXh3Xffxfbt2+Hs7IwBAwaAiFBWVobz588j\nOjoazzzzjHAgoip99knOap6VartNtU9rpINGbBpFeno6NTY2trm8oaGB0tPTW1128+bNdu+/rXU4\na97ZadOmUVZWFtXV1bVYVldXRz/88ANNmzat1aw++yRnNc+acttS7Hc0IalP/h2toqICffv25Sxn\nGTOazrJPS+oH399++034f11dHVasWIHJkycjMTER169fF82+9tprwv/z8/Ph4eGBoKAguLm5IScn\nh7MWnG3u7Nmz2Lp1K06ePNnuuvrsk5zVPCvVdneWfbpVOn9nMIGAgADh/8888wzFxsZSdnY2LVq0\niB599FGNs1FRUbR9+3YiIsrNzaWQkBDOWnD2vvvuE/6/bds2cnNzo7i4OJLL5bRhwwaNt6vPPslZ\n8axU222qfVoTku38hw4dSrW1tURE1NjYSL6+vlplVYkdAs1Zy8qOGjWKzp49S0RE5eXl5Ofnp9V2\n9dknOStOiu021T6tCUkN9bx69Sq++uorEBFu3LiBbt26AYBwwQwxZ8+exZQpU0BEOH/+PK5fv47b\nbrsNRIT6+nrOWnBWVV1dHQYNarq2n4ODA6ysxCuj+uyTnNU8K9V2d4Z9ui2S6vzHjh2Lb775BgAw\nZswYXLhwAU5OTigrK8Ptt98umk1PTxf+/+yzz6KhoQEA8Ndff+Gpp57SOLt48WKds823m5CQ0Omz\nzR+vObb5t99+g62tLYCmA2nKysrQv39/1NbWorGxUTSrzz7JWc2zUm23qd5LmrDo0T6Mibly5QpO\nnDiBkJAQUzeFsQ4nqdE+hvLEE0+ILq+vr8dHH32EF198Efv371dbpvqLfGuuXr2K1157DR999BHq\n6+uxfPlyREdH46WXXsKNGze0bquHh4dG6+kzQuGDDz5AeXk5AOD06dMYO3Ys7O3tMXLkSBw7dkw0\ne//99+Ozzz5DTU2NRu1UdebMGcyZMwcvvvgiqqur8fjjj2PIkCGYNm1au+cwb2howIYNG3Dvvfdi\n6NChCAwMxMMPP6zX9U7t7e316vh37NjR7jpVVVU4c+ZMi/mqr19bzp8/j4qKCgBNr9OXX36JgoIC\n7RsKIDExUaecNiOj/vjjD2Gfb2xsxIYNGzB//nz85z//0aiMkZGRgZs3db981r59+3Dq1CkAwE8/\n/YQ333wT3333nUbZmpoafPHFF3j33XexZs0aZGZmtvutEDDde0kjev9qIBGXL19u9Xbp0iUaMGCA\naDY+Pp5mzpxJ77zzDg0bNoyeeeYZYZnqjzKtmTp1Kv3rX/+ihIQEGjNmDCUkJNDevXtp8eLFNGfO\nHNGsjY0N2drako2NjXCzsrIS5ovRZ4SCt7e38P+oqCj66quvqLGxkfbs2UOjR48WzQ4YMIAefPBB\n6tOnD02bNo2++uor4Qey9tx9992UnJxMr7/+Ovn4+NCbb75J586do//+978UEREhmo2NjaWXX36Z\n9u3bRwsXLqQXX3yRfvjhBxo/fjytWbNGo+13NBcXF9HlW7Zsof79+5O/vz95e3tTbm6usKy9/eq9\n994jV1dXcnd3p7Vr15JcLqf4+Hjy9PSklJQU0ez8+fNb3Ozs7Gj+/Pm0YMEC0aw+I6N8fHzo2rVr\nRET03HPP0YMPPkibNm2iuLi4dt8LREQ9evSgvn370iOPPELfffcd1dfXt5tRWrhwIYWEhNDw4cPp\nxRdfpJCQEHr11Vdp/PjxtHjxYtHsli1baMSIEfTYY4/RXXfdRbNmzaKYmBjy9fWlo0ePimZN9V7S\nhMV0/jKZjNzc3Fq9WVtbi2ZVf9Gvq6ujuXPn0v333083btxo902qHC3S2NhIjo6O1NDQIEy3N5Jk\nwYIF9Oijj1JZWZmQcXNza/exEuk3QsHDw0P4//Dhw1t9PO1t9+rVq5SSkkKRkZHUr18/iouLox9+\n+EHjNjfvONsb3dD8MQUHBxNR0xGQnp6eoll9REdHt3nr2bOnaHbo0KFUWlpKRE3D9zw9PWnr1q1E\n1H7n7+PjQzU1NVReXk49e/YU7qeioqLdrLOzM8XExNDGjRtp48aN9Omnn5KDg4MwLUafkVGqHWFg\nYKBa591eVrntiooK+vjjjykiIoJuv/12evLJJyk7O7vdrLe3NzU0NFBNTQ317t2bampqiKjp/ezj\n4yOa9fX1Ff5olZeXk0KhICKio0ePtjvk0lTvJU2YRdmnrKys3bPd3XXXXcjOzkZRUVGLm6Ojo2j2\n1q1bwv+tra2xbt06+Pv7Y/z48e1+JVOOFpHJZIiKilKbbs/777+PhQsXIiYmBmvWrNHoa6aScoTC\n1q1btR6h8NBDDyEuLg5nz57F/fffj3fffRfnzp3Dp59+ijvvvFOj7dvZ2WH27Nn4/vvvcfLkSYwc\nORKrVq0SzRARTp06hQMHDuD69ev49ddfAQCFhYXttrlbt244ffo0AODQoUPCeVC6d+/e7ogdffz0\n00948sknsXjxYuH27LPPYvHixbCxsRHNNjQ0oH///gCA4OBg7NmzBytXrsSaNWva3W63bt3Qq1cv\nODg4wN3dXbifPn36gNr5GS8/Px8ODg7IzMyEQqFAXFwcbGxsEBsbi9jYWA0fufYjo1xcXLBr1y4A\nwKBBg/Dnn38CAC5duqTxycn69OmDJ554Art378bRo0fh7e2N559/Xu064K1R7vddunRRew9YWVlp\ntO0ePXoAAHr16iWUcYYOHYqrV6+K5kz1XtKI3n8+OoFx48aRq6ur6Ne3Dz74gI4cOdLqsvbKAjEx\nMcIBFqrWrVtHXbt2Fc3Gx8dTVVVVi/mFhYU0ZswY0axSfX09vffee3T33XeTk5OTRpnY2FiKi4sT\nbspvD6WlpTRu3Lh28xs2bKDg4GDq168f2djYkJeXFy1dupSuXLkimgsNDdWofa357rvvyM3NjUaN\nGkUHDx4kb29vGjx4MPXv35++//570eyuXbto4MCBNHjwYHJ1daVffvmFiIj++usveu6553Rqz+zZ\ns+mpp56iY8eOtbnOxIkTadeuXa0uu/vuu0XvPyQkhE6fPq027+rVqzRu3Lh2v40OGzZMOBfRn3/+\nKcy/fv16izHhbTl48CCFh4fTG2+8QXfeeadGGWXZ0cbGhrp27Sp847h582a7n2TPnTtHYWFhdPfd\nd1N0dDT17t2bwsLCyN/fn3bs2NHutsW+0RQVFYlmFyxYQGPGjKERI0bQv//9bxo9ejStWLGCJkyY\nQE8//bRodsmSJaRQKGjFihU0ZswYWrlyJRERXbp0qd1vDUSmeS9pwmxG+zQ2NuLEiRMYMmSIqZui\nMSLS6nSspaWlyMvLw6RJkwzYqs6jsbER5eXluP322zX69N7Y2IjLly/DwcFB59Pcqjpw4AD++OMP\nHDhwAG+88Ybe99dcXl4eevXqBblcrja/rq4On3/+OR555JE2s+fOncOAAQNgbW2tNr+kpAQnTpzA\nhAkTNGpDY2MjkpOTkZOTg88++0z7B/E3bUZG5efno6CgAPX19Rg4cCCGDx+OLl26tJvbs2cPIiIi\ndG5jdnY2HB0d4e3tjX379iEnJwdeXl6YMmVKu9nvvvsOJ06cgL+/PxQKBYCm566urk74ViA1ku38\nf/zxR5w+fRpz5sxBeXk5ampqhK+gjOlDeTANY+ZMkjX/ZcuW4Y033hDqXnV1dZg1a5aJW8Wk7uef\nf4aPjw88PT0BNH0ynzdvnolbxZhhSLLz//rrr5Geno5evXoBAJydnQ03FpZZjKeffhqZmZlwcHAA\nAAQEBGDv3r0mbhVjhiHJzr/5CI5r167pfF+ajBTirOVkm4/AaO+ygozpwlTvB1WS7PynTZuGJ598\nEleuXMEnn3yC8ePHY+7cuTrd1yOPPAJPT088++yznLXw7J133ikcwV1XV4e33noL3t7eWm8TAGJj\nY5GQkIDff/+dswbMmnLb+mRN9X5QY9CxRAb0ww8/0OLFi2nx4sWUlZWl1301NDTQ77//zlkLz/71\n1180c+ZMuv3228nBwYFiYmLo0qVLOm0zNzeXvvjiC52GmXJWGtvWt92mej8oSXa0jz70GSnEWfPO\n6kufkUKclca2tckqz73UFrFLMuqT1YhefzqMrFevXmrnuVG9tXeuG6VXXnmFoqOjSS6XExHR+fPn\nNb4qDmfNM9vauW6Ut/bOdaO0f/9+8vb2Fk5LceTIEUpISOBsB2el1m5XV1dyc3MjV1dXkslk1Ldv\nX+rbt69wuhlDZTUhqc6/IwwdOpQaGhrUjhbU5LwinDXf7Keffqp2nhvVW3vnulEaMWIEnTt3Tm27\nmhz9yVntsqbctj7ZuXPn0nfffSdMb9++nR5//HGDZ8VIeijDX3/9pXaKV03OlaHPSCHOmmc2Li5O\n4/sXo89IIc5q1xVJrd2//PIL1q1bJ0xHRUXhueeeM3hWjCRH+2RkZEAul2PQoEEICwuDm5sboqKi\nNMrqM1KIs+ad/euvv/Dss89i0qRJiIiIQEREBMaNG6dRVp+RQpzVblSVFNs9YMAAvPbaayguLkZR\nURFWrlwJZ2dng2dF6f3dwQT8/PyovLxc+Pq1e/dujc4HrqTPSCHOmm92woQJtG7dOvL09KTs7GyK\ni4vTeCSHPiOFOKvdqCoptvvSpUu0YMECCggIoICAAFq4cCFdvnzZ4FkxkhztExQUhEOHDsHf3x+H\nDx9Gly5dMHToUI2ufsRYW4YNG4bDhw+r7UvDhw/HwYMHTdwyxjqeJGv+ffr0QXV1NUJDQzFr1izc\ncccd7Z473cbGps0zPcpkMlRVVXHWQrNKymseODk54dtvv8WAAQNQWVkpmlmwYEGby2QyGd5//33O\ndkDWlNvWJ7to0SKsWbMGkydPbjWbkZFhkKwmJNn5b9u2DT179sS7776LzZs3o6qqCq+88opoRp9z\n/3DWvLNK//73v3HlyhW8/fbbWLBgAaqqqvDuu++KZoKCgoQ/Os2/RLd3WmnOap6VarsfffRRAMDi\nxYtF1+vorCYkWfZRqqqqEq6yJZPJtDroQZeRQpy1jCxjlkCSn/w//vhjvPLKK2rD+mQyGc6ePdtu\nNiMjA4sXL0ZpaSnuuOMOnDt3Dt7e3jh+/DhnLTS7YMECyGSyVi9/qEk5Amj6Y/PGG28gPz8fN27c\nELK7d+/mbAdmpdZuPz+/NpfJZDLR3yn1yWpE75+MTWDw4MFUXl6uU1afkUKcNc9s165dKSAggF5/\n/fUWB3tpepCXPiOFOKt5VmrtLioqEr0ZKqsJSXb+CoWCampqdMoOGzaMiJqOBK2vrycizY8e5ax5\nZsvLyyk5OZnCw8Np/Pjx9Mknn1BlZaVG21MKDAxssa2goCDOdnDWlNvWt92djSTLPklJSQgJCUFI\nSIgwQkPTr+e6jBTirHlnHRwckJCQgISEBJw/fx5paWnw8fHB6tWrhR/d2qPLSCHOap+Vart/+eUX\nLFy4EPn5+airq0NDQwNsbGw0GoWmT1aUqf/66CIoKIieeeYZ2rBhg/AVXdOv59XV1VRfX091dXX0\n6aef0po1azQ+UIOz5p09ePAgPfvss+Tv70/x8fF0/PhxjXJERBkZGVRZWUm//fYbhYWFUWBgIKWn\np3O2g7NSbfewYcOooKCAAgICqL6+njZs2EDPP/+8wbNiJDnaJzAwEEeOHNHrPvQZKcRZ88q+9NJL\n2L59O7y9vfHwww9j4sSJsLa21nhbjLVHeWCq6gGEAQEByMvLM2hWjCTLPlFRUfj4448xZcoUdO/e\nXZivSeegz0ghzppnduXKlRg0aBCOHj2Ko0eP4oUXXhCWtTeqQp+RQpzVPCvldgNAr169UFtbC39/\nfyxZsgROTk6t3l9HZ8VI8pO/m5tbqwdXFBUVtZt1d3dHTk6OcJFubXDWPLPFxcWiy93c3NpcZm1t\nDV9fX0yfPh0DBgwA8M+BQDKZDLGxsZztgKyU2w0A586dwx133IG6ujq8++67qKqqwrx58+Du7m7Q\nrBhJfvJv780q5q677kLPnj05y1mBq6tru0dqElGr65SVleGLL77A559/ji5dumDGjBmYNm0a7O3t\n290uZzXPSrndAHDw4EFER0ejd+/eWLZsmcY5fbNiJPnJ/9q1a3jnnXfwxx9/YN26dSgsLMSpU6cQ\nHR3dbvbw4cOIi4vTaaQQZ80zGxYWhujoaNx3333w8PBQW3bq1Cls27YN3333Hfbt2ye6beVIoXfe\neUerkUKc1S4rxXbHxcVh9+7dCAsLw4wZMxAZGanxtQD0yYqR5Cf/OXPmICgoCD///DOApvNdP/TQ\nQxp1/k888QQmTJgAPz8/WFlZtfmJjrOWk83KysLmzZvxf//3f/j9999ha2sLIkJNTQ18fX0xa9Ys\n7Ny5U/Q+Dh06hLS0NOzYsQNRUVEICgrSqL2c1S4r1XZv3LgRdXV1+P7775Gamop58+ZBoVBg/fr1\nBs2K0nu8kAkoD+RRvZza0KFDNcqqZrTFWfPOEhHV19fThQsX6MKFC8JBYmJefPFFGjZsGM2aNYu+\n+eYbqqur03hbnNU8K+V2q6qtraWMjAyaOnUq9e3b12jZ1kiy7DN69Gjs2rULo0ePxpEjR3DmzBnM\nnDkTBw4caDebmJgIV1dXnUYKcda8s7qwsrLCoEGDcNttt7VY1t5IIc5qnpVyuwFg+/bt+Pzzz7Fn\nzx6Eh4djxowZuOeeezQq3+iTFSPJzj8rKwsrV65Efn4+FAoF9u/fj40bNyIiIqLdrD4jhThr3lld\n6KRtrz4AAAySSURBVDNSiLOaZ025bX3bDQAzZ84U6vU9evRod/2OyoqRVOd/69Yt4eCby5cv45df\nfgEAjBo1SqdhgYzpizT4TaGtdTiredaU29a33Z2VpC7gPnLkSOH/y5YtQ3R0NKKjo7Xq+K9du4YV\nK1bg8ccfBwAUFhbi22+/5SxndRIeHo4333wTBQUFLZadOnUKq1evRlhYGGf1zEq53QCwdetWyOVy\n2NnZwdbWFra2trCzsxPNdERWlN6/GhiR6o95uv6wN23aNEpKSiIfHx8iIqqpqdH4x2LOmndWFzdv\n3qT169fThAkTyMnJieRyObm7u5OTkxNNmDCBPv30U6qtreWsnlkpt5uI6K677qL8/HzRdQyRFWNx\nnb8+I4U4a95ZfWk7UoizumVNuW1ds6NHj9ZqOx2VFSOpcf4nT54Urm5z5swZtSvdaPqre/fu3YWr\n8CjvR3VUCGctN6uvLl26wNHRkbMGzppy27pmhw8fjhkzZmDq1KlqBx8+8MADBs2KkVTnf+LECb3v\nY9myZYiMjMT58+cRExMjjBTiLGcZM5SrV6+iZ8+eyMrKUpuvSQeuT1aMpEb7kB6/uuszUoiz5p1l\nzBJJqvPX5xwsw4YNw+HDhwE0naL1gw8+0Hi7nDXvLGOGsmDBgjaXaXIqaV2zmpBU2Uefc7Co/o37\n6aeftNouZ807y5ihDB8+HID6/qm8NkB7VQx9spqQVOffvXt3xMfHIz4+Hg0NDbh06RKApmuwdunS\nxcStY4wxdSUlJYiKikJgYKBRs5qQVNlHHz179hQufnDmzBkMHjxYWNbeSCHOmneWMUNJS0tDZmYm\n8vLy4O/vj0mTJuGee+5Bnz59DJrVhMV0/lI8LwhnjZNlzNCICEeOHEFmZiZ27NiB+vp6KBQKREZG\nIjg42GBZMRbT+eszUoiz5p1lzNiuXr2KHTt24IcffsC6deuMllUlqXP76EOK5wXhrHGyjBnD/v37\nsXnzZvy///f/sG3bNly/fl3jzlufbFss5pN/bW0tNm/ejNTU1DZHCsXExAhH0HHWcrKMGdojjzyC\ns2fPIiAgQG1wiiZDkvXJirGYzl+VPiOFOGveWcYMwdvbG/n5+TqVHfXJipHUUM+OIrXzgnDWeFnG\nDMHX1xdlZWUYMGCAUbNiLLLzZ4wxYyovL4ePjw+Cg4OFEw3KZDJkZGQYNCvGIss+jDFmTHv37kXz\nrlYmk2k0CEGfrBj+5M8YYwZWWFiIsLAwyOVyo2bFcOfPGGMG9scff+DJJ59EUVERhg8fjrFjxyI0\nNBQBAQEGzYrhsg9jjBnJjRs38Mknn+Ctt95CaWkpGhoajJJtDXf+jDFmYCtWrMDPP/+MmpoaBAQE\nIDQ0FHfffbdGI3j0yYrhzp8xxgwsMDAQ1tbWuPfeezF27FiMHj1a48uL6pMVw50/Y4wZQVVVFfbv\n348ff/wRX3zxBRwdHTW+9oQ+2bbwD76MMWZgx44dw48//oh9+/bh4MGDcHFxwdixYw2eFcOf/Blj\nzMCio6MRGhqK0NBQjBgxQrjetKGzYrjzZ4wxI6itrUVBQQFkMhk8PT216sT1ybaFyz6MMWZg2dnZ\niI2NhaurK4CmsfspKSkaHaWrT1YMf/JnjDEDGzZsGFJTU+Hp6QkAKCgowMMPP4zDhw8bNCvGYi7m\nwhhjplJfXy903gDg4eGB+vp6g2fF8Cd/xhgzsDlz5qBLly545JFHQETYvHkzGhsbsWHDBoNmxXDn\nzxhjBnbz5k2sXbsW+/fvBwCEhoZi3rx5Gh2spU9WDHf+jDFmgXi0D2OMGdhPP/2E5cuXo7i4WKjX\ny2QynD171qBZMfzJnzHGDMzT0xPvvfcehg0bpnZNaQcHB4NmxfAnf8YYMzB7e3tERUUZPSuGP/kz\nxpiBLV26FA0NDXjggQfUfqgdNmyYQbNiuPNnjDEDCw8Ph0wmazF/z549Bs2K4c6fMcYM7ObNm+jR\no4favMuXL6Nfv34GzYrhI3wZY8zAHnjgAdy6dUuYLisrg0KhMHhWDHf+jDFmYPfffz+mT5+OhoYG\nFBcXY+LEiUhKSjJ4VgyXfRhjzAg+/PBDZGZm4ty5c/joo48wZswYo2TbwkM9GWPMQN5++20ATQdl\nERH+/PNP+Pv7IycnB7m5ufjXv/5lkKwmuPNnjDEDqa6uVhupc//990Mmk7WY39FZTXDZhzHGLBB/\n8meMMQM7deoU3nrrrRbn59m9e7dBs2L4kz9jjBnY0KFDkZCQoHZ+HplMhqCgIINmxXDnzxhjBhYU\nFIRDhw4ZPSumy7Jly5Z1+L0yxhgTlJWV4fjx4xg4cCBu3bqFGzdu4MaNG+jZs6dBs2L4kz9jjBmY\nm5tbixE6mp6TX5+sGO78GWPMAvFoH8YYM5CtW7e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      "text/plain": [
       "<matplotlib.figure.Figure at 0xada013cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uAMjURRAEwWfoCllP8MXUZW5ujsTERAQHB4Mxhj59+mDq1KkAyNRFEDLI5kXo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\nAEAEMJABAIgABjIAABHAQAYAIAJ+AbxH0LssjW8KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xade642ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readmission_counts = pd.crosstab([df['gender'],df['age']], df.readmitted.astype(bool))\n",
    "readmission_counts.plot(kind='bar', stacked=True, color=['green','red'])\n",
    "\n",
    "# divide the counts to get rates\n",
    "readmission_rate = readmission_counts.div(readmission_counts.sum(1).astype(float),axis=0)\n",
    "readmission_rate.plot(kind='barh', stacked=True, color=['green','red'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top chart is a repeat of the cross tabulation of the age and gender of the patients against the number of patients that were readmitted.  The cross tabulation shows actual patient counts, as opposed to the bottom chart which shows readmission rates.\n",
    "\n",
    "The bottom chart is another version of the age/gender bar chart in the previous graph, but with the readmission rates on the same bar.  This is not as easy understand as the simpler bar chart above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0xaf33450c>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5RIP4+HgcO3YMUVFRJlIOWVlZHer/yy+/xLx58yAUCs2e18s2AMCyZcuwdu1ao7G1J+UQ\n9PCzF4BxAKY/9GUP/3RWv9zBxtPVPouP+T3qG8g28N2XyWScjltQUBBspkunQgPsmaKtZ9KkSSST\nySyeZynazJgx6y5jr+Mc7EmorZTD3LlzIZVKER4eDiJCfHw8l6LdWSkHALhy5QrkcrlJ0VJDKYft\n27ejoKAAIpEI3t7eRuqr7Uk5kMETHIPBYDDsR7dJORARtFot97mlpYVrZ4uUQ58+fTB69GiEhIRg\nzJgxuHr1KgBjKYcNGzZgzJgxaGxsRGNjI/r27QvAupQDnzEswcFHWHzOC59jA/gfn604pZQDACQn\nJ+Pdd99FTEwMmpqazK7vfPrpp/D29saFCxewd+9erFu3Dnl5eb1ayoHBYDAcCaeUcqioqIBWq+VE\n8iy9sisoKOA2ryYlJeH1118HwKQcGAxnxxmlD9g+IfM4pZRDZWUlvLy8kJSUhKqqKsycORNbtmzh\n5ML1yOVyBAQEAABEIhEkEgkUCgUGDhzYe6UcGAwe0JulD/iG3Sah9qQciAixsbGYPXs2oqKi8P77\n72PQoEFQq9X47W9/i8zMTLz77rsW+9ZoNDh8+DDKy8sREBCAhQsXIjs7G0uXLu3w+KxKOXg9/OwG\nYBB+1QGpevins/rHeBYPi8+xxvcovv5zR9o/xJFSlq35hmtCjjCeroinK1K0nVLKobS0FOvWreN+\nIP/4xz9QUlKCTz75xKhdXFwcMjIy8PTTT0Oj0cDf3x+3b98G0IulHKrQa4XDeAGf4+tMbBnOl8Uq\nk8l4/UrO1nV2uz0J+fj4oKGhgfNHjRqFjz/+GOnp6WhtbUVxcTFWrVoFAKiuruZe0+3fvx+hoaEA\ndNlxO3bsQE5OjlHf48ePR319Perq6uDj44MDBw5g4sSJJmOYO3cucnJy8PTTT+Orr77i1pD09wwM\nDDQ/+IxHDJ7BYNgVsURsvZGDwecJ6FGw6z4hvZRDaWmpkfwCALS0tODNN98EoJNyuHPnDoRCIdzd\n3fHjjz8CsLxPSCgUIigoiJPqHjZsGP7+978DMN4n5OLigj179mDPnj0QiUTYsGEDgF+lHGJjY82O\n3dl+w2IwGAxnxa77hAIDA/HYY4+hT58+nPyCUqnEzZs3IZFIOPmFuLg45OXlQalUQqFQYNKkSQAs\n7xOSyWSoq6uDUqlEU1MTJBIJjh49CsB4n5CrqytWrFgBlUqFxsZGrmxPb5Zy4PteBRaf88Ln2AD+\nx2cr3SblYIg5+QVzTx+WpBzMyULo688Zot8Y25beLOXAYDAYDoWtdYKsYU7KQU9b+QVbpBwsyUIY\nwqQcmDFj5sjGp3pzgG3Tid0mobZSDnrMyS90VsqhPVkIQ2yVckgBaMND+xDGJealzGc+85nfRT4A\nI4kHqVTqNL5UKqWUlBRKSUmhDRs2kENOQsOHDzc5/tFHH9GKFSssXieTyWjOnDnt9p2ZmUnvvfce\n52/atIm2bt3a7jUajYYkEgnnNzU1kZ+fn0k7GPwD4aNJe/j+LD4WX2+MzVJ8tn5xOyK2xmK3NaG2\nKdp6cnNzsWjRIqNj1dXVgC4CkxTtlJQUkz5GjRqF4uJirvxPcXExQkJCTNoZ7gEqKCgwatNeiraA\nxxbtAGNg8bH4eltsluIbIHa+VPOupttStIuLi/Haa6/h4sWL+P3vf4///ve/2Lt3L+bOndvpFO32\n0r0NU7SXL1+OoqIiuLi4QCQSIT09HQBL0WYwGAxHwa4FTAMDA1FTU8OlaFdUVAAA7t69i+HDhxul\naCcmJppU0baUoq1P9zbXl75gKQDMnz8fQ4cOxfbt242u780p2gwGg+FIOGWKtrW+DPs0129vTtHm\n+14FFp/zwufYAP7HZzNdtirVBnunaFvqyxCWos2MGTN7m1jSsTRrwywzPgLYNp3YtYDplClTUFlZ\naXS8uroa4eHhqK6uhlAoBKBLIDCsoj1s2LB2q2i315chCoUCYrEYLi4u2L17N/bu3YsDBw4AAJqb\nmxEYGIja2lqja3hfwJTBYHQtGWDryIDjFTAFzP/FfPnll5g3b57RpKGvduDq6oq0tDRkZWV1qH9z\nfRkycOBA7vOyZcu4sj36sVkUr+OzlAPzmc/8rvcf4khSC/b2HV7KQavVYsiQIVz6tZ6nn34amZmZ\nmDZtGnfMsIr2qlWr4OHhgc2bN1usot1eX4bon7AAYP/+/fjzn/+Mn376CQBw+fJlLFq0CKWlpUbX\n8P5JqAr8lQIAWHzOjLPGltGxJyEm5WAeu6Zojx07FufPn+cUVK9cuQK5XG4yaSxZsoRLYIiIiMDm\nzZsBWE7Rbq8vwxTt7du3o6CgACKRCN7e3tysDegy76ZOnWp+8Bm2xcxgMHofzigr4UjY9XXcggUL\nEB8fj4sXL6K4uBirV6+Gj48PIiIijPYJBQQEoKqqChKJBGfOnMHFixcRFhZmMUUbAPr06YPRo0cj\nJCQEAoEAP/zwAwIDA41StDds2IBLly7h5MmTcHNzQ9++fQHo1qtWr16NwsJCs32z97sMBqOr4fNT\n0KPQLWtCAoEA0dHRXEp02709AoEAWVlZJvuEtm7darHv5ORkvPvuu4iJiUFTU5PZ9Z1PP/0U3t7e\nuHDhAvbu3Yt169YhLy8PAoHA5kdHBoPBYHQddp2E8vPzUVRUZDJBdHSfkCUqKiqg1Wo5pVRLr+wK\nCgq4J6OkpCS8/vrrAABfX19s27YNubm5iIyMNLnOYsICg8FwWAaIxVDcv9/Tw7AI39eEbMVum1W1\nWi3Onj2LESNGmJzLy8szqR+Xnp6O8PBwrF69Gmq1ut2+Kysr4eXlhaSkJERGRmLt2rVobW01aSeX\nyxEQEAAAEIlEkEgkUCgUAICJEyfi0KFDZvvv8Y0HdjSpA4yBxcfis0dsdx88AMP5sNuTUF1dHcRm\nivNVV1fj7NmzmD17Nnfs/fffN9onlJmZ2e4+IY1Gg8OHD6O8vBwBAQFYuHAhsrOzsXTp0g6Pz9/f\nH1euXDF7LhVA0MPPXgDGAZj+0Jc9/NNZff0xRxlPV/v6Y44ynq729cccZTxd6U/viv4cKIW5ra9P\na3aU8Tyq31Up2narmGBPKYeSkhKaNm0a53/++ef02muvmbSbPXs2HTt2jIiIWlpayMfHhzvXW6Uc\nmDHjq9nx64zRAWz9+TullMP48eNRX1+Puro6AMCBAwcwZswYk3Zz587l9hh99dVX3BqS/p69UcqB\nGTO+mqPLIrDaceax2yRkKOWgmySBo0ePory8HK+++irGjBmDa9euAQAiIyPh6uoKDw8PfPbZZ3ju\nuecAWN4nJBQKERQUhCFDhsDNzQ1lZWV4+eWXAejSsr/77jsAgIuLC/bs2YO+ffti2bJlXBKCXsrB\n0j4hIuKtSaXSHh8Di4/FZ4/YHDkpgWGZbpFy0GebrV+/HoWFhSZp1Z2VcpDJZKirq4NSqQQRISoq\nCkePHsW0adOM9gm5urpixYoVTMrBAL5n57D4nBc+xwbwPz5b6TYpB3Np1e7u7lxbIjK53pKUg5+f\nH9RqNVQqFZRKJVpaWrjyPIbof0NqS2+WcmAwGAxHwq4p2hUVFTh+/DgEAoHVtOrOpGiPHj0as2bN\ngr+/PwYPHoy4uDiuNJAhAoEA+/btQ1hYGObPn48bN24A0O0T2rdvn8UUbf1mVmbMmPU+8/TyfIRv\nPsuwNSHzdFuKdntp1Z1N0T506BCkUinkcjmICLGxsZg9ezaioqKM2iUmJmLx4sWclENKSgon5dBe\nijava8dVAXiypwdhR1h8zouDxPYgg+036k7s+jrO8FVYQEAAxo0bh6CgIAiFQjz//PM4efIkAFMp\nh7Kysnb7LSkpQXx8PDw8PNCvXz/Ex8fj2LFjJu0GDhwIFxcXADophxMnThiNTSAQmL/Bfuh2zkkB\nHINxufYqJ/dh5byz+7By3tl9WDnvzP6TDjIeA2QymdETzKP4+r01XdVfT/symQypqalITU1FRkYG\nbIbsRFtlVY1GQ+Hh4XT79m0i0qmp7ty5k4iIbt68SUREra2ttHLlSkpPTyciotLSUkpOTjbp+9tv\nv6WZM2eSRqMhtVpNMTEx9P3335u0q66u5j5//fXXNHnyZM6/dOkSU1ZlxoyZiXVUKZVhDGDbdGLX\nFG29lIPez8rKQkxMDMLCwiAQCLi06iVLliAsLAxhYWFQKBR45513AFhO0Z47dy7Gjh2L8PBwjBs3\nDuPGjcOzzz4LwDhFe/v27Rg7dizGjRuHTz75pMNSDuQA6ar2Mj6n+LL4nNscJbb79fZJ9WZrQuax\n6+s4vZQDEQEARowYAT8/P2g0GpSWluLmzZsAdK/qGhoaIBKJOCkHwHKKtlQqhUwmg4uLC1xdXbFj\nxw4UFBQAADZu3IjExETufrW1tRAIBFAoFDhy5AiAX6UcFi9ebM/wGQwGg2EFuymrAsDu3buxZcsW\nXLp0CQKBANOnTzeRX3B3d0daWprZfUIdQS8LIZfLjapyA0BOTg5OnDhhsk/o1q1biIiIwHfffWdS\nRVsgYBIPDAaD0Vls/e7sNikHa/ILtn7xm5OFMOzTXL9MyoHBYHQER5eH4APdJuXQlfuEDDEnC6FH\nIDC/TwhgUg58NRaf85ojxtaV8hBsTcg8dpuELO0T+uCDD3D8+HFcvnyZSxR4//33UVlZiePHj0Oh\nUCAzM7ND9zAnC2FIYmIirl69itOnTyM2NtaoGKo1KYeMh/YRfi0Xj4efndkvd7DxdLXP4mN+V/oA\n7Jbi7Oy+w6dot5Vy6Kj8gqwDUg56rMlCGKLRaEgikXA+k3JgxoyZNbPjVyTvsPVn1W1SDu3JL3RW\nykGPOVkIQ2pqarjPBQUFCAkJ4Xwm5cCMGTNr5ujyELyga+dCY2bMmEHjx4+n1tZWIiL64osvqH//\n/tS3b1/y8vKiS5cuERHRoEGDyMXFhdzc3GjAgAFUUlJCRET5+fn0yiuvmO37yJEj1LdvXxo9ejSF\nhITQlStXiIjoj3/8IxUUFBAR0Zo1a8jT05NcXV3J09OTDh48SES6p7Tw8HB66623TPq184+kx5FK\npT09BLvC4nNe+BwbEf/js/W70677hAIDA/HYY49x2Wa7d+/GN998g+bmZsjlcvj7+wPQSTnk5eVB\nqVRCoVBg0qRJACzvEwJ+lYXQF0n19fUFYLxPKCgoCC+++CJUKhV2796NXbt2AejdUg4MBoPhSNg1\nRfvq1au4f/8+iAjnzp3rdIr21q1bzfZrLd1bT0FBAacvlJSUhNdffx2AsZSDuQ2rLEWbwXA8xBKx\n3aoZdAdMT8g8dpuE9FIO+vUewxTtqqoqzJw5E1u2bEGfPrqHsfT0dGzatAkxMTHYsmULXF1dLfZt\nrS89crkcAQEBAACRSASJRAKFQsFJOfzmN78xf4OMR4+fwWB0Lay6NT9xSimH9vrqKO1KOewH4PXw\nsxuAQfi1xHzVwz+d1T/Gs3hYfI41vkfx9Z/baa9PD9Y/VTiTb5jq7Ajj6Yp49NtsgoKCYCt2K9tT\nW1uLqKgoXLhwAQBQWlqKdevWcUH84x//QElJCT755BOj64qLi5GVlcUVITVHR/uKi4tDRkYGnn76\naWg0Gvj7++P27dsAAKVSiSeffNIogw54+Cou4xECd3Sq4BCaLXaDxee8WIstw/xre2dBJpPx+pWc\nw5XtaS9F28fHBwcOHMDEiRMB6NKl/f39zaZo79ixAzk5OUZ9t9eXIXPnzkVOTg6efvppfPXVV9wa\nkv6ellK0eT0JMRhOilji3OnSfJ6AHgW7TUJCoRAhISGYMGECysrKIBQKcfr0aW6NxtfXF3//+98B\nAJGRkbhz5w6EQiHc3d3x448/ArAs5SAUChEUFIQhQ4YAAIYNG8b1tWHDBowfPx6JiYlwcXHBnj17\nsGfPHohEImzYsAGA7ilt3rx5iI2NNTt2Z/5ti8FgMJyJbk3R7tevH5RKJZRKJa5evQqRSDcHdjZF\nWyaToa6uDkqlEk1NTZBIJDh69CgA4xRtV1dXrFixAiqVCo2NjVi7di2A3p2ibfhemo+w+JwXPscG\n8D8+W7HrJHT16lXcvn27Q08WllK0x44da3Lcz88ParUaKpUKSqUSLS0tnER42z7N9WuYos1gMBiM\nHqQLNsrvXwMRAAAgAElEQVSapa28NxGRSCSiyMhIevrpp+mbb77hjqemplJwcDCFhYXRqlWrSKVS\nWe1//fr15OXlRRKJhN555x2zbbKzs8nf359CQ0PphRdeoOvXr3PnLl++zOS9mTHjgQ0QMzluRwCw\nbTqxetWDBw9Io9FwvkajoYaGBqsd19TUUHBwsNGxmzdvEpFuAggKCuLK9lRXVxMRkUqlopSUFNq0\naVO7fRcXF9MzzzxDjY2N1NDQQJMnT6bDhw+btLtz5w6p1WoiItq1axfNmDGDO6dUKsnX19fkGqDn\niyYyY8as42brlx+ja7H178Hq67iYmBgolUrOb2pqsrig3xbduH5FX6bnySefxPTp07nXYfpXaa6u\nrkhLS0NZWVm7/ZaUlCA+Ph4eHh7o168f4uPjcezYMZN2AwcOhIuLCwBg2bJlOHHihNHYLFVGSAV/\npRz4Fk9bn8XnvL7MynlLviGOJHXQ1td/dpTxdEU83SLlEB4e3qFjbWn7Ou7u3bvU3NxMRES3b9+m\n4OBgOnfuHBH9+oTU2tpKK1eupPT0dCIiKi0tpeTkZJO+v/32W5o5cyZpNBpSq9UUExND33//vUk7\n/RMWEdHXX39NkydP5vxLly5ZfB3X07/Z2dOkDjAGFh+Lrytj68DXmEPACpiax2qKdr9+/XDixAk8\n9dRTAICff/4Z7u7uVic3oVCIsWPH4vz58xg5ciTOnTuHFStWoE+fPmhtbUV6ejpGjRoFAFiyZAmX\nwBAREYHNmzcDsJyiPXfuXEilUoSHh4OIEB8fj2effRaAcYr29u3bUVBQAJFIBG9vb253L6DLvJs6\ndarZsbPKcQyG8+Ascgtsn5B5rE5CH330ERYsWMC9SquursbevXs71PmCBQsQHx+PS5cuYfLkyfjP\nf/6DsLAwCIVCfPvtt0hLSwMABAQEoKqqChKJBGfOnMHFixcRFhZmMUVbKpVCJpNxr9p27NiBqVOn\nYu7cuVzBUgAYMWIEamtrMWTIECgUChw5coQ7tnr1ahQWFpodt25SZzAYDIa9sToJTZgwAefOncP5\n8+cBACNHjmy3uKghbb/MPTw8zKZFCwQCZGVlYd68eUbHLVXRjo6O5vq5e/cuhg8fjlmzZpntd9Gi\nRdi+fbvJcVtLTDg7fC8dwuJzXvgcG8D/+GylQxUTXF1duVI6APCvf/2rQ8kJ+fn5KCoq6pA0gq0T\nQn5+PhISEuDm5ma2T3P9+vr6Ytu2bcjNzUVkZKTJeSblwGDwC2eXgeAzNhUwDQgIwPXr19tto9Vq\nMWTIEE7KAQBcXFwQFhYGV1dX/OEPf8Bzzz0HAEhLS8PRo0fh7u7eISkHQ2bMmIG33noLCQkJJudy\ncnKQnp4OHx8fjBw5Eh9++CFX6qeqqgq/+c1vUFpaanQN7wuYMhi9kQz2mt3e2Pp2yeIkpC99Y44D\nBw6gqamp3Y5ra2sxZcoUVFZWcsf0hUqrqqowY8YMHDhwAEOHDkVNTY2RlMOwYcPalXIw7C88PBzV\n1dUQCoUm5xUKBcRiMVxcXLB7927s3bsXBw4cAAA0NzcjMDAQtbW1RtcIBAIgHPyVcmA+83ujn6Ob\nhPTpxY4gheDsvkxmLOWwcePGrp2EBgwYgM8//xz9+/f/tfHDmW7BggW4detWux23lXJoS1paGubM\nmYOkpCSj4x2RctDzl7/8BefOncPf/vY3q221Wi28vb1RX18PgEk58BYWn/Niz9gyev5JiO9rQl0u\n5TBp0iR4eHiY/aGNHDnSasdtpRzq6+vh7u6Ovn37oq6uDkePHsW6desAdF7KQU9ubi4yMzMtjkH/\nhAXopL5DQkK4c0zKgcHoPTi7DASfsTgJFRUVWbzo8OHDVjtuK+Vw7tw5PPPMM1wCwahRo7h9Qp2V\ncgCAo0ePory8HK+++ioEAgF++OEHBAYGGu0T2rZtG3bt2oXm5ma4ubnhm2++AcCkHBgMRvfD56eg\nR8FuekKATsqhpqYGAoEAkydPRv/+/fHggalOfFxcHBITE01StC3tEwKA9evXo7CwEDExMWhqauIy\n2gz3CQUFBeHFF1/Ezp07sXfvXuzatQvR0dG9WsqBwWAwHAmrteOOHTuGCRMmoF+/fnBxcUGfPn3g\n6enZoc7tJeVQUVEBrVbLKaV6eHiYreJQUFCAlJQUAEBSUhKXlNCbpRwM60DxERaf88Ln2AD+x2cz\n1ur6REZGUmVlJY0bN440Gg3t2bOH1q1bZ7UekD2lHPbv309z5syhefPmUUREBK1Zs4a0Wq1Ju7Fj\nx5JcLuf8YcOG0Z07d4iISTkwY9abrSfkH1jtOPN06HVccHAwtFothEIh0tLSMG7cOGzZsqXda+rq\n6iBuU9Pp2rVrRinaoaGhGDp0KN5//32jFO3MzMx2U7Q1Gg0OHz6M8vJyBAQEYOHChcjOzsbSpUs7\nEg4AXUXvK1eumD2XAiDo4WcvAOMATH/oyx7+yXzmM995/eiHywLdmdKsT2vurvvZ22+bom0z1map\nKVOmUHNzMy1ZsoTWrFlDH3zwAYWFhVmd3Wpqamj48OEWz6emptJXX31lclwmk9GcOXPa7bukpISm\nTZvG+Z9//jm99tprJu1mz55Nx44dIyKilpYW8vHx4c41NTWRn5+fyTVAz1cTZsaMmX2tA199jE5i\n68/U6prQZ599htbWVnzyySfw8PDAjRs3sG/fPquTm7kUbZVKBQBcivaYMWMAgKuqQGSaoq1f0zFk\n/PjxqK+vR11dHQDd5ll9X4bMnTuXS+/+6quvuDUk/T0tpWgLmDFjxmvricrbbE3IAl07FxozY8YM\nGj9+PLW2ttJPP/1EoaGhFBoaSiKRyEjldNCgQeTi4kJubm40YMAAKikpISKi/Px8euWVV8z2/dxz\nz1Hfvn2pb9++FBISQi0tLURE9Mc//pEKCgqIiGj37t3k6upKrq6u5OHhQZmZmUSke0oLDw+nt956\ny6RfO/9Iehy+v5dm8TkvfI6NiP/x2frd2ekU7ZSUFHh4eOC1114zm7lmSNsU7dOnT2PlypUIDQ3F\nwIEDuXadTdGWyWSoq6uDUqkEESEqKgpHjx7FtGnTjFK0XV1dsWLFCpMq2r05RZvvexVYfM4Ln2MD\n+B+frVh9HdeW1157DTExMfjss8+stm2bon3ixAncunXLrOyCvo0hllK0/fz8oFaroVKpoFQq0dLS\nwlVGaNunuX57c4o2g8FgOBQdfWRqbGzs1CNW2xRtrVZL06dPJ7lcTtnZ2fT6669z5zqbok1EtH79\nevLy8iKJRELvvPOO2TbZ2dnk7+9PoaGh9MILL9D169e5cyxFmxmz3mViSfenZRvCXseZx+rruJ9+\n+gnLly/HgwcPcP36dZSXl2P37t3YuXNnu9e1TdHeuXMnEhIS8Pjjj5s8nXQ2RfvQoUOQSqWQy+Ug\nIsTGxmL27NmIiooyapeYmIjFixdzVbRTUlK4DavtpWjzunZcFfhbABNg8Tkzdo7tQYZptRZGz2N1\nEvr973+PoqIiTvtn3LhxKC4u7lDnhpNNSUkJDh8+jJ07d6KhoQFqtRpisRibN2/mXqW5uroiLS0N\nWVlZ7fZbUlKC+Ph4rq5cfHw8jh07ZjIJGa47LVu2DGvXrjUam0Xxuv3gr5SD/pijjIfF1zlff8xR\nxtOV/pP2v19P7qth+4TMY1XUbuLEiSgrK0NERAS3hhIeHo5Tp06127E5UTs9OTk5+Pnnn/Hxxx8D\nMK6ivWrVKnh4eGDz5s0Wq2gXFBTg448/RlFREVpbWxEfH49Vq1bh2WefNWpnWEV7//79+POf/4yf\nfvoJAHD58mUsWrSIidoxGL2FDJhdI2Z0DV0u5aDniSeewNGjRwEAarUa27dvx+jRo612LBQKMXbs\nWJw/f96s9IPhU8iSJUu4BIaIiAhs3rwZgOUq2nPnzoVUKkV4eDiICPHx8dwEZFhFe/v27SgoKIBI\nJIK3tzc3awO6zLupU6eaH3yG1fAYDIaT0dNyDnzXE7IVq9lxf/3rX7Fjxw7I5XIMHjwYv/zyC3bs\n2NGhzhcsWID4+Hij2fH+/ftYv3690bGAgAA0NDRAJBLhzJkzuHjxIgDLKdpSqRQymQwuLi5wdXXF\njh07UFBQAEBXRVuvCjtixAjU1tZCIBBAoVDgyJEjAHRSDqtXr8bixYvNjpseZtXx0aRSaY+PgcXH\n4uuJ2O7X3+/Q9xaje7H6Ou5R2L17N7Zs2YJLly5xTz4rV65EXV0dBg4cyL2OS0tLM7tPqCPcvXsX\nw4cPh1wu57SK9OTk5ODEiRMm+4Ru3bqFiIgIfPfdd4iMjDQ6Z+sjJYPBYPRm7PY67o033uA6108k\nnp6emDBhApesYIn8/HwUFRVx1+n3CcXFxeHnn382amvrF39+fj4SEhJMJiB9n+b69fX1xbZt25Cb\nm2syCQHGrwoZDAb/GCAWQ3GfPRk5AlZfxzU3N6O8vBwjRozA8OHDcerUKdy4cQOffvopfv/731u8\nTqvV4uzZsxgxYgQAoLW1FW+99RY++OADs+3T09MRHh6O1atXQ61WdziAvLw8LFq0yOw5gUCAffv2\nISwsDPPnz8eNGze4cxMnTsShQ4fMXtfjGxrsaFIHGAOLj8XX07HdNSOuaW9Y7TjzWH0SOn36NI4e\nPQqRSNf0d7/7HaKionDkyBGu0Kg57LlPSE91dTXOnj2L2bNnmz1v6z6hVABBDz/zTcqh3MHG09U+\ni4/5HfH1OFLKs7P53SblMGLECLp79y7n3717l4KDg4mIaNy4cRavayvl8OKLL9ITTzxBQUFB5OPj\nQ56enpSenm5yXUekHPR89NFHtGLFig611Wg0JJFIOJ9JOTBj1nutA199jE5i68/U6pPQ2rVrERER\ngenTp4OIUFxcjLfffhuNjY2YOXOmxevaSjn84x//4D7r9wnpU7EN9wm1lXIwt09IT25uLjIzMy2O\nwXCfUEFBAUJCQrhz1qQcGAwGf+kJKQeGBToyU8nlctqyZQt988039MUXX1BxcXGHZjhDKQc99+7d\nIy8vLyNhPFukHI4cOUJ9+/al0aNHU0hICF25coWIjKUc1qxZQ56enuTq6kqenp508OBBImJSDnyG\nxee88Dk2Iv7HZ+t3p9Unob///e/Yvn07rl+/joiICJSUlGDy5Mk4ePCg1QnOUMpBz7vvvouEhIRH\nknIAgPXr16OwsBAxMTFoamri7mEo5RAUFIQXX3wRO3fuxN69e7Fr1y5ER0f3aikHBoPBcCSsZsf9\n5S9/QVlZGYKCgiCVSvHLL79AIpF0qHN7STlUVFRAq9VySqkeHh5wd3c3aVdQUMApsyYlJXFJCb1Z\nyoHvO7ZZfM4Ln2MD+B+frVidhNzc3Lgv+ObmZowaNQrnz5+32rFWq0VFRQWOHz8OgUDQpSnalZWV\n8PLyQlJSEiIjI7F27Vq0traatJPL5QgICAAAiEQiSCQSKBQK+Pr6Yt++fRZTtAUCATNmzLrRPL08\nrX6nMPiJ1ddxAQEBuHv3Lp5//nnExsZiwIABCOpAOp49U7Q1Gg0OHz6M8vJyBAQEYOHChcjOzsbS\npUutjksPk3LgKSw+p+RBxgPe11bje3y2YnUS2r9/PwAgIyMD06dPx/379xEXF9ehzg0nm66UcggI\nCMC4ceO4yfD5559HSUmJySQ0ePBgXLt2DY8//jg0Gg3u3bvHrUUR9VIphxoHGw+Lr3M+X+N7iCPt\ng2G+g+wTspW2yqqGtFVWvXnzJhERtba20sqVK7n9Q6WlpZScnGy27/DwcLp9+zYR6ZRZd+7cadJu\nx44dXHZdbm4uLVy4kDt36dIlpqzKjJmDWE+rnjIeHcBO2XG2Yk8pB6FQiKysLMTExICIMH78eLz8\n8ssAjKUcli1bhpdeegnBwcHw9vZGXl4e10d7Ug5kJkmCwWAwGF2P1cSER8FQyuHq1at46qmnEBER\nga1bt2LYsGFcu85KOQC6zDeNRgONRgOxWMyVFTKUcsjNzYVUKkX//v2hUqm4tHJrUg58hu/1q1h8\nzgufYwP4H5+t2O1JCDB+onj88cdRUlICFxcXNDY2YsyYMUhKSsKQIUMgEAiQlZVlsk9o69atZvuV\nyWQ4efIkzp49CyJCVFQUiouLMW3aNKN2AoEAixYtMpFy0GfksCceBoPB6FnsOgkZSjm4uLhwx5VK\nJVxcXIxetXVmQvDz84NarYZKpYJWq0VLSwuX3GAIEZNyYDAY5uluOQeWGWceu72OayvlAAA3btxA\nWFgYnnjiCaxatcqoakJn9gmNHj0as2bNgr+/PwYPHoy4uDiL605MyoEZM2bmrCfkHBhm6JK0CDPU\n1NRw1bbbcvPmTQoODqYLFy4QEVF1dTUREalUKkpJSaFNmza123dxcTE988wz1NjYSA0NDTR58mQ6\nfPiwSbs7d+6QWq0mIqJdu3bRjBkzuHNKpZJ8fX1NrgFAKQBteGgfAiTFr9V3pU7u8y0eFh9/fGk3\n3w8ASaVSo5pu9vT1n7vrft0RT0pKCqWkpNCGDRvI1unErpOQoZRDW5YuXUr5+fkmxzsi5ZCZmUnv\nvfce52/atIm2bt3a7jVMyuHX/4Q9PQYWH4vPEWKz4+/gZmEFTM1jt9dxbaUc5HI5lEolAODu3bs4\nevQowsLCAOhkFR4+lZlIOehrvxkyatQoFBcXc+tBxcXFRjINempqarjPnZVy4KtFO8AYWHwsPkeI\nrbvlHNiakHnsuk8oJCQEEyZMQFlZGWQyGV5++WXoJkxg3rx53HpRZGQk7ty5A6FQCHd3d/z4448A\nLO8TEovFOHXqFPr37w8AaGlpwZtvvgnAeJ/Q8uXLUVRUBBcXF4hEIqSnpwPQpWjPmzcPsbGxZseu\nHyODwWAw7ExXPo61JS0tjeLj44mISK1Wc+szDQ0NFBgYSNevXyciXcWDffv2mVy/Zs0aOnPmTLv3\nUCgUNHDgQFIqlSbnsrOz6Y033jB7XWBgIH3xxRcmx+38I+lx+P5KgMXnvPA5NiL+x2frd6ddU7Sv\nXr2K+/fvg4hsStG2tE/IkPz8fCQkJMDNzc3kHJH5FG1DKQdzG1ZZijaD4byIJWLcr+++1GvGo2G3\nSUgv5aBf7wF0KdoJCQm4ePEisrKyTFK0N23ahJiYGGzZsgWurq4duk9eXh7eeusts+f0KdrFxcUY\nOXIkPvzwQwwZMoSTcvjNb35jvtOMDofJYDAcjAcZjpl6zdaEzCMgc48KXUBtbS2mTJmCyspKk3PV\n1dWYNm0afvjhBwwfPhw1NTVGUg7Dhg1rV8rBsJ/w8HBUV1dDKBSanFcoFBCLxXBxccHu3buxd+9e\nTtiuubkZgYGBqK2tNbpGIBAA4eBvFW3mM5/vfs6vb1Ycqeo03/y2VbQ3btxo03q6XSehqKgoXLhw\nwez5ZcuWIT4+Hi+88ILR8eLiYmRlZeG7776zeo+//OUvOHfuHP72t79ZbavVauHt7Y36+noAuleC\nTz75pFEGHfBwEsqw2p3zUgVe6tFwsPicl66KLcMxk4v4ridkayk0u72OM5eiPXDgQLi7u3Mp2uvW\nrQOge6Lx9/c3m6K9Y8cO5OTkmL1Hbm4uMjMzLY5B/4QFdC5Fm9eTEIPBc8SS7k29ZjwaTpmiDQBH\njx5FeXk5Xn31VQgEAvzwww8IDAw0StHetm0bdu3ahebmZri5ueGbb74BwFK0GQxG98Pnp6BHwa5S\nDoGBgXjssccgEAiwYMEC3Lt3D0qlEnV1dTh69ChXyy0uLg55eXlQKpVQKBSYNGkSgPalHNavX4/C\nwkJUVFTg+PHj8PX1BWAs5RAUFIQXX3wRKpUKu3fvxq5duwDoCqDW19cjIiLCnuEzGAwGwwp2nYSu\nXr3KidW5uLhwadqdSdEeO3asyfGKigpotVrExMQAADw8PODu7m7SrqCggKu4kJSUxCUlGKZo9zb4\nrmnC4nNe+BwbwP/4bMUpU7QrKyvh5eWFpKQkVFVVYebMmdiyZQv69DGeU+VyOQICAgAAIpEIEokE\nCoXCaoo22yfE6G66W1aAwXAYHm2PrGXsWUU7Pz+fJBIJVVVVkUajoaSkJPr0009N2o0dO5bkcjnn\nDxs2jO7cuUNE7VfR7s4iisyYEbq/mCaD0dXY+m+425RVDfH398eUKVNQXl6O4cOHcxlsrq6uSEtL\nQ1ZWVrv9BgQEYNy4cQgKCgIAPP/88ygpKcHSpUuN2g0ePBjXrl3D448/Do1Gg3v37nFPX0Rk8Ykn\nFUDQw89eAMYBmP7Qlz38k/nM70pfjyPtA2E+8zuzT8hmunQqNECj0dCgQYM4/8aNG9TU1EREunpv\nI0eOpPPnzxOR7smIiKi1tZVWrlxJ6enpRERUWlpKycnJZvsODw+n27dvE5Gu9tzOnTtN2u3YsYNe\neeUVIiLKzc2lhQsXcucuXbpEEydONLkGDvBbsT1N6gBjYPGZWkf/K/K5/hifYyPif3y2Tid2TdEe\nO3Yszp8/j5EjR+LcuXP43//9XwgEAggEArz99ttcivaSJUu4BIaIiAhs3rwZgOUUbaFQiKysLMTE\nxICIMH78eLz88ssAjKtoL1u2DC+99BKCg4Ph7e2NvLw8ro+ysjJMnTrV7NjZihCju+luWQEGw1Gw\n6+u4BQsWID4+HpcuXYKPjw/69euH+/fvQygUGhUcDQgIQFVVFSQSCc6cOYOLFy8iLCys3RTtgoIC\naDQaEBHEYjFEIl0oGzdu5Nrk5uZCKpViyJAhUKlUOHjwIJYuXYra2lqsXr0ahYWFZvvWTeoMhuPB\n570mfI4N4H98ttJta0L9+vXD559/jmHDhqG6uhpPPfUU4uLi4OnpCYFAgKysLMybN8/oektVtGUy\nGU6ePImzZ8+CiBAVFYXi4mJMmzbNqJ1AIMCiRYuwfft2k+O2lphgMBgMRtdh10koPz8fRUVFEAgE\nCA4O5o77+/vD19cXt2/fhqenJ4DOPX34+flBrVZDpVJx6qr65AZDiMxLOfj6+mLbtm3Izc1FZGSk\nyXmWos1g8ANHknXge+04W7HrPqGzZ89y6z6GlJWVQa1WY9iwYdyxzuwTGj16NGbNmsXVm3vjjTcw\ncuRIk3aWpBwAYOLEidi2bZv5G2R0Llanogr8LYAJsPicGTvE5qiyDoxfsVvFhLq6OojNLLZWV1cj\nOTmZS+0DgPfffx+VlZU4fvw4FApFu0VJAeDQoUOQSqWQy+WQy+U4cOAAjhw5YtIuMTERV69exenT\npxEbG8tVTwB0T2NXrlwxf4P9AKQP7Rh+LRePh5+d2YeV887uw8p5Z/dh5bwz+0/aoX8YVyqQyWQ9\n5uvTmh1lPI/qy2QypKamIjU1FRkZGbCVbpVyuH//PqKjo7F+/XqT9R89HZFy2Lp1K9RqNd555x0A\nwHvvvQc3NzesWbPG4jVMyoHB6IVksESj7sLmdfZHzQ23RNt9QiqVimbMmEEfffSRSdvO7hP69ttv\naebMmaTRaEitVlNMTAx9//33Ju30lRiIiL7++muaPHky57e3T4gZM2b8MLFE3IlvLfvC9gmZx26v\n4wylHIgIf/7znyGVSvGHP/wB7u7uePLJJ3H69GkAOikHV1dXeHh44LPPPsNzzz0HwPI+IbFYjFOn\nTqF///7w9PSETCaDVqsFoNsnpH+KWr58OUQiEdzd3ZGcnIw5c+YA+FXKwdI+IXqY0MBHk0qlPT4G\nFh+Lr7tic5SkBIZl7PY6DgCWLl2Kmpoa/PDDD7hw4QL69OljlKL93//+F56enkhLS0NiYqLJK7q1\na9ciOTnZbCVtPXfv3sXw4cMhl8uN9h4BQE5ODk6cOGGSog3oykxs3rwZixcvNjrOUrcZDAaj89j6\n3dltUg7BwcFcNpxhirYec4O3JOVgSH5+PhISEkwmIH2f5vrtzVIODAaD4VCQnWi7JmRIaWkpjR49\nmvNTU1MpODiYwsLCaNWqVaRSqTp8n+joaCosLDR7Ljs7m/z9/Sk0NJReeOEFun79Onfu8uXLbE2I\nGTNmJjZAbJ91JLYmZB67ZsdNmTIFlZWVRserq6sRHR2Nzz77DBMnTgQA1NTUYNCgQVCr1fjtb3+L\nYcOG4d1337V6j+rqaoSHh6O6uhpCodDkvEKhgFgshouLC3bv3o29e/dywnbNzc0IDAxEbW2t0TUC\ngQAp4G8V7Y/Ar3ja+iw+5/X1n3t6PNEA9walq6tO63GEKthdEY9hFe2NGzfatpTRhROhETU1NTR8\n+HCjY/fu3aPIyEjat2+fxetkMhnNmTOnQ/f46KOPaMWKFR1qq9FoSCKRcH5TUxP5+fmZtANAxIwZ\ns15rdvxa5DW2/tzstibk4+ODhoYGzler1fif//kfJCcnmyQg6NVXiQj79+9HaGgoAF1lBcMNpm3J\nzc3FokWLLJ433ANUUFCAkJAQo3sGBgaavU7AjBmzXmusonk307VzoTEzZsyg8ePHU2trK/3pT38i\ngUBAbm5u5ObmRkFBQXTq1CkiIho0aBC5uLiQm5sbDRgwgEpKSohIp6Cq1wNqy5EjR6hv3740evRo\nCgkJoStXrhAR0R//+EcqKCggIqI1a9aQp6cnubq6kqenJx08eJCIdE9p4eHh9NZbb5n0a+cfSY/D\n9/fSLD7nhc+xEfE/Plu/O+2aHRcYGIjHHnsMAoEACxYswIULF6BUKnH58mWoVCpOjS8uLg55eXlQ\nKpVQKBSYNGkSALQr5bB+/XoUFhaioqICx48fh6+vLwCdlENiYiIAICgoCC+++CJUKhV2796NXbt2\nAdAVQK2vr0dERIQ9w2cwGAyGFZwyRbuiogJarRYxMTEAAA8PD7i7u5u0Kygo4F7nJSUlcUkJvTlF\nm+9VfFl8zgufYwP4H5+t2G0S0mq13FNKW2kES1W0w8PDsXr1aqjV6nb7rqyshJeXF5KSkhAZGYm1\na9eitbXVpJ1cLkdAQAAAQCQSQSKRQKFQwNfXF/v27cOhQ4fM9q/XG2LGjNmv5unl2dmvAQbDKnaT\ncsoTqu0AABV7SURBVLBWRfuzzz7jjr3//vtGKdqZmZntpmhrNBocPnwY5eXlCAgIwMKFC5GdnY2l\nS5d2eHztVtHO6HA3zkcVgCd7ehB2hMVnN+wtiyDjud4O3+OzlW5TVgV0VbTnzJmDzZs3c3uEAHCC\ndK6urkhLS0NWVla7/QYEBGDcuHEIerim9Pzzz6OkpMRkEho8eDCuXbuGxx9/HBqNBvfu3cPAgQO5\nsQkEgrZd69gP3QYhAHADMAi//sevevins/o1nWzvbD6Lz66+I+1TYb5j7ROyFbttVtVqtRgyZAiX\nfq1WqxEfH4+5c+di5cqVRm2rq6s5gbpVq1bBw8MDmzdvRllZGXbs2IGcnByTvp966in8+9//ho+P\nD9LS0jBx4kS8+uqrRu127tyJM2fO4K9//Svy8vLwzTffIC8vDwBw+fJlLFq0CKWlpUbXCAQCfj8J\nMRi2kmF+7ZbBAHTfnbb8+7Dbk5BQKMTYsWNx/vx5jBw5El9++SUOHz4MhULBzZ45OTkICwvDkiVL\nuASGiIgIbN68GYDlKtpCoRBZWVmIiYkBEWH8+PF4+eWXAeiqaI8fPx6JiYlYtmwZXnrpJQQHB8Pb\n25ubgADdupSlKtpsEmIwTBFL2P4ZRtdj1+y4BQsWID4+HkSEJUuWYMaMGbhy5QqGDBmCX375BWFh\nYQB0r9caGhogEolw5swZXLx4EUD7KdoFBQXQaDTQaDQQi8UQiXTzqWGKdm5uLqRSKfr37w+VSoWD\nBw8C0JUUWr16tUkFbT1EPV/W3l7GZykAFp99zd6yCIZlbfgI3+OzlW5dE1q7di2ampq4/Tp6BAIB\nsrKyTCopbN261Wy/MpkMJ0+exNmzZ0FEiIqKQnFxMaZNm2bS76JFi0ykHPTZPm3Hx2AwGIzuxa6T\nUH5+PoqKirgEgBkzZlj8baAzE4Kfnx/UajVUKhW0Wi1aWlq45Ia2fZrr19fXF9u2bUNubi4iIyNN\nzltMWGAweMQAsRiK+44j+sb3zDG+x2crdt0ndPbsWYwYMaJD7TuzT2j06NGYNWsW/P39MXjwYMTF\nxWHkyJEm7QQCAfbt24ewsDDMnz8fN27c4M5NnDjR4j4hYsasF9jdB/ZNuWYwOkK37xMyR2f3CR06\ndAhSqRRyuRxEhNjYWMyePRtRUVFG7RITE7F48WJOyiElJYWrmtDePqFUAEEPPzMpB+fyWXyd9B0s\n5VePI4yHxdc9KdogO2FOyoHIulSDtfNERJmZmfTee+9x/qZNm2jr1q3tXsOkHHQmdYAxsPgcIz47\n/ve3Cb4X+OR7fLb+e7Lb67i2Ug4Gk57Jsc5KOYwaNQrFxcXcelBxcbGRTIMeJuVgatEOMAYWn2PE\n52iSBXxfM+F7fLZi131CISEhmDBhAsrKyiAQCDBgwADcu3cPAoEAAQEB2LNnD2JjYxEZGYk7d+5A\nKBTC3d0dP/74IwDL+4TEYjFOnTqF/v37AwBaWlrw5ptvAjDeJ7R8+XIUFRXBxcUFIpEI6enpAHQp\n2vPmzUNsbKzZsZubKBkMBoPR9dh1n5ChlAMA7Nu3DwUFBUhISMD169e5SaCzUg7R0dG4desWlEol\nbt68CYlEglmzZgEw3ic0f/58/O53v4NSqcSDBw/w9ttvA+jdUg5836vA4nNe+BwbwP/4bMWuk5Ch\nlAOgS9HWP720xdzThyUpB0Py8/ORkJAANzc3s32a67c3SzkwGAyGQ9FVi1Jt0Wg0NGjQIJPjUqnU\nJPEgNTWVgoODKSwsjFatWkUqlarD94mOjqbCwkKz57Kzs8nf359CQ0PphRdeoOvXr3PnLl++TBMn\nTjS5BujxzFlmzHqdiSXiDv+fZzgmgG3Tid0KmNbW1mLKlCmorKw0Oi6TyfDBBx/gu+++447V1NQY\npWgPGzas3RRtPdXV1QgPD0d1dTWEQqHJeYVCAbFYzKVo7927l0vRbm5uRmBgIGpra42uYQVMGYwe\nIANsLdbJcbgCpoD5f1TmqhF0VspBz5dffol58+aZnYAAcLINALBs2TKsXbvWaGy9UsrhGM/iYfE5\n1vhs9WG8ZuII+2C62udbfA6/T6gzr+Nu3rxJREStra20cuVKSk9PJyKi0tJSSk5OtniPSZMmkUwm\ns3i+urqa+/z111/T5MmTOf/SpUvsdRwzZg5iYomY9/to+B4fYNt00m1SDgAwZcoUnD9/Hg0NDUYp\n2p2VcgCAK1euQC6XmxQtNUzR3r59OwoKCiASieDt7c3N2kD7Ug7EXgswGIwuhu0TMk+3STkAQL9+\n/dDS0oKYmBijFG1bpBz69OmD0aNHIyQkBGPGjMHVq1cBGKdob9iwAWPGjEFjYyMaGxvRt29fANal\nHBgMBoPRPdh1Emr7RLF27Vp8/vnnJu30Ug6//PKLkc5QeynaycnJWLduHSoqKnD8+HH4+vqatPn0\n00/h7e2NCxcuYNWqVVi3bh13v94q5cD3vQosPueFz7EB/I/PVpxSyqGiogJarRYxMTEAYPGVXUFB\nATZu3AgASEpKwuuvvw6ASTkwGAzHxdEkNuyNU0o5VFZWwsvLC0lJSYiMjMTatWvR2tpq0k4ulyMg\nIAAAIBKJIJFIoFAoADApB2bMmDmm9TaJDaeUctBoNDh8+DDKy8sREBCAhQsXIjs7G0uXLu3w+Hqr\nlAPzmc98x/f1OFJKdlvf4VO07SnlUFJSQtOmTeP8zz//nF577TWTdrNnz6Zjx44REVFLSwv5+Phw\n55iUAz+Nxee8xufYOhOfHb+W7Yqt43ZKKYfx48ejvr4edXV1AIADBw5gzJgxJu3mzp2LnJwcAMBX\nX33FrSHp78mkHPhnLD7nNT7H1pn4HE1iw9702D6hIUOGYODAgTh16hSWLFmCgwcPws3NDe7u7njm\nmWcAWN4nJBQKsWrVKgQGBqK1tRXu7u5ISkoCYLxPaNmyZQgICMCnn34KFxcXBAYGoq6uDj4+Pti0\naRO8vb3Njt3cRMlgMBiMrsduteMAIDs7G7W1tVxqtCF79uzBnTt3sGbNGgA6jaAHbRbk1q5di+Tk\nZLNp2qmpqZg8eTJWrFiBc+fOISEhAVVVVSbtoqOj8cEHH5hkwS1YsAD/+c9/8J///MfoeG9N3WYw\nGIxHwdbvTrvuE1q8eDEKCwvNDiw3NxfPPfdcu9e3t0/I398f9+7dAwDU19dj8ODBFvtpe/9bt26h\nsbERTzzxhMkkxHf4vleBxee88Dk2gP/x2YpdJyFXV1ccOnTIZN+NufTt5uZmPPXUU5g8eTK+/fZb\nq32np6cjJycHAQEBePbZZ/Hxxx9bbJuSkoKIiAj86U9/AqDbJ1RYWGgxTVu/mZWPFh0d3WV9eXp5\ndvSfAoPBYJjFrptVLWEuffvatWvw9/dHVVUVZsyYgdDQUAwdOtRiH6tXr8by5cuxatUqlJSUYMmS\nJWafar744gs8/vjjaGhoQFJSEj7//HO89NJLAIDHH38cly9fNu08HPytot2F/oOMBw6VMqpHJpM5\nzHhYfB33DStNO8J4WHzdk6Jt1zUhS9TW1iIqKgoXLlwwez4tLQ1z5szhkg3MERISgn/961/ca7hh\nw4ahtLQUPj4+Fq/JycnBzz//zD01/e1vf8OVK1ewZcsWrg3TE+oEGSyJg8Fg6HBIPSFLtE3frq+v\nh7u7O/r27Yu6uv/f3rnFNJV1cfxfpYZLlVHxAYF4AUxhiJ0qWPEWQSItRlMzOvEyExodVDQhvpho\nfLCZJ5xo8skYL4lIIvGWyHyCUXhwhjJGVIw3VCpggqSieAX91FGh7O/B4UihpYfTobvndP2SnXS3\nu/usf07b1bPP2mu9xOXLl4Vghh07dsBgMMBsNrvModVqcfHiReTm5sJut+Pjx48DHJDT6URHRwei\noqLQ1dWFc+fOYfHixcLrT58+de/Brf+aVEUzOjLwQkn7XiUoESXrU7I2QPn6pDKs94Q8MXLkSCQn\nJyMtLQ2MMdjtdiE8Oy4uDhEREdBqtQCAe/fuITo6esAc33zzDbZs2YKwsDDo9XohSAEA9Ho9gC/3\nmaZMmYLQ0FCMGTMGf/31F5YvXw4AKCoqwtmzZzF//vwBczPGFNuqq6v/tbnedgZPfiuCIIYHLstx\nALBu3Tq0t7fjwoULANyHaAOA0WhEVVXVoHPt378ft2/fxpEjRwa85ilEu62tDdOmTcP79+9dnqcQ\nbYIgiKETkCHag9Ha2ioUshsMbw4IAE6cOIHVq1d7fN3dMcrKypCQkBB0IdoEQRCBBBcn5HQ6hTpA\nveHbQw3R7qW1tRWPHj1CZmamxzH9Q7QBoKCgAGazOehCtJXeRnso66EUeqOTlIiStQHK1ycV2YZo\n93Lq1CmsXLlScGb9kRKireTFOBu+ZuxVIqq//+ZtAkEQQ4Dbclz/JbLe4IMpU6Zg4cKFuHXrlqh5\nTp8+PehS3MSJEwEAGo0Ga9asQV1dnYsN7pyXBV8C5KwA/gPX9Oo2mffh5XW59wHXf5w2m01RfSXr\n6917Eij2kL7B+zabDRaLBRaLBVarFVLhEpjgdDoRGxsrZM/uH6I9Z84cVFRUQKvVegzRBoAHDx7A\nZDK5zRnXe5y+IdqrV6/G4sWLsWHDBgBfkp1GR0dj06ZNwntUKpWir4SUjgq0d4kgeCCrwIS+GbYB\nwG63Iy0tDd999x0yMzOxY8cOryHagOeroL4h2kajETqdDnq9HnFxccjLyxPG1dXVuQ3R5p3ynZr0\npgkLG3A+lUTff6VKQ8naAOXrkwq35bgffvgBJpMJjDGkp6fj/v37UKlUGDlypEtgQldXFwwGg9s5\nkpKSUF5ejpSUFKxdu1Z4vncpLyIiAocPH0ZPTw8+ffqEnp4eYfnt119/RWNjo9s6RLz38shln1Ag\ntnP/hPwTBCEPuAQmAAOXTMLDw93eB/IUot3c3IzCwkLU1tYiMjJSKHDXn/z8fBQXF2PWrFnIyclB\nVVUVjEaj7wJkitJ3bJM++aJkbYDy9UmGcSIrK4s1NjYKfY1GM6T3b9u2jRUXFw865smTJ0yr1Qr9\nkydPso0bNwp9o9HI7t275/Ie/FNelxo1asproyNHD+l3hhAPIM2dcLkSGqyUw6hRo7B9+3avtYaa\nm5uhUqkwb948OJ1OWK1WZGdnu4xpa2tDbGys0I+JiUFbW5vQ7y3lMGBJzipdW8DTgq8ZsZUI6ZMv\nftD2P+vArCz+wmaj3HHukO0+oe7ubjx8+BA1NTVwOBxYsGAB7t69i8jISNF2eCzl8F8ot5RDQ4DZ\nQ/qG1leyvnZ8ZbiOB1dn0Dd8erj7t2/fFmwIhFIMvvZttiAv5ZCfnw+DwQCLxQIAyMrKwu7duzFz\n5kxhzNOnT5GZmQm73Q7gSzXXmpoaHDp0CECQlnKoBpDB24hhhPTJF39os4JbCL/VavVpP02gE3Sl\nHMxmM06ePAmLxYKXL1+iqalpwJVTdHQ0xowZg2vXrmHWrFkoLS1FQUGB8HrQlnKo4W3AMEP65Msw\nawvE8iPBjmz3CWVnZ2P8+PH49ttvkZmZiT179mDs2LEAvu4TAoADBw7g559/RmJiIhISElwi4zzt\nE2IBEGo8XC03N5e7DaSP9PHSxrP8yKNHj7gdO6BhnCgpKWGFhYVex2VnZw/L8d+8ecNSU1MHPK/T\n6bhH8FCjRo2a3JpOp5P0W8ytntDnz5+RlZWFmpoaj8lHh5OioiKMGzcOP/74o9+PTRAEQXyBmxMi\nCIIgCG5pewiCIAgiaJ1QVVUVtFotEhMTsXv3brdjCgoKkJiYCJ1OJ7q0RKDgTd+DBw+Qnp6O0NBQ\n7N27l4OFvuFN3/Hjx6HT6TB9+nTMnTsX9fX1HKyUhjdt5eXlQlLemTNn4s8//+RgpXTEfPcA4Pr1\n6wgJCcHvv//uR+t8x5s+m82GyMhI6PX6AcU25YCY82ez2aDX65GSkuJ9g64vN/flSnd3N4uPj2ct\nLS3s8+fPTKfTsYaGBpcx58+fZyaTiTHG2NWrV5nBYOBhqiTE6Hv+/Dm7fv0627lzJ9uzZw8nS6Uh\nRl9tbS3r7OxkjDFWWVkpm/MnRtu7d++Ex/X19Sw+Pt7fZkpGjL7ecRkZGWzJkiXszJkzHCyVhhh9\n1dXVbOnSpZws9A0x+jo6OlhycjJzOByMMcZevHgx6JxBeSVUV1eHhIQETJ48GWq1GqtWrRpQUryi\nogK5ubkAAIPBgM7OTjx79oyHuUNGjL4JEyYgNTUVarWak5XSEaMvPT1dyJ5hMBjw+PFjHqYOGTHa\nIiIihMfv3r1DVFSUv82UjBh9APDbb79hxYoVmDBhAgcrpSNWH5PprXgx+k6cOIHvv/9eSJnm7fMZ\nlE6ora0NcXFxQj82NtYlp5ynMXL5IROjT84MVV9xcTFycnL8YZrPiNV29uxZJCUlwWQyoaioyJ8m\n+oTY7155eTny8/MBgEv0rFTE6FOpVKitrYVOp0NOTg4aGhr6TxOwiNHX3NyM169fIyMjA6mpqSgt\nLR10Tm6lHHgi9kPd/9+KXL4McrFTKkPRV11djaNHj+Ly5cvDaNG/h1htZrMZZrMZly5dwk8//SRs\n/A50xOjbunUrCgsLhTQwcrpqEKNvxowZcDgcCA8PR2VlJcxmM5qamvxgne+I0dfV1YWbN2/ijz/+\nwIcPH5Ceno7Zs2cjMTHR7figdEIxMTFwOBxC3+FwuGTbdjfm8ePHiImJ8ZuNviBGn5wRq6++vh55\neXmoqqoSsmkEOkM9d/Pnz0d3dzdevXqF8ePH+8NEnxCj78aNG1i1ahWAL8mOKysroVarsWzZMr/a\nKgUx+vombzaZTNi8eTNev36NcePG+c1OqYjRFxcXh6ioKISFhSEsLAwLFizAnTt3PDqhoAxM6Orq\nYlOnTmUtLS3s06dPXgMTrly5Ipsb24yJ09fLrl27ZBeYIEZfa2sri4+PZ1euXOFkpTTEaHv48CHr\n6elhjDF248YNNnXqVB6mSmIon03GGLNYLKysrMyPFvqGGH3t7e3C+bt27RqbNGkSB0ulIUaf3W5n\nixYtYt3d3ez9+/csJSWF3b9/3+OcQXklFBISgv379yM7OxtOpxPr169HUlISDh8+DADYuHEjcnJy\ncOHCBSQkJCAiIgIlJSWcrRaPGH3t7e1IS0vD27dvMWLECOzbtw8NDQ3QaDScrfeOGH2//PILOjo6\nhPsKarUadXV1PM0WhRhtZWVlOHbsGNRqNTQaDU6dOsXZavGI0SdnxOg7c+YMDh48iJCQEISHhyvu\n/Gm1WhiNRkyfPh0jRoxAXl4ekpOTPc5JGRMIgiAIbgRldBxBEAQRGJATIgiCILhBToggCILgBjkh\ngiAIghvkhAiCIAhukBMiCIIguEFOiCAIguAGOSGCIAiCG/8HQ/zgXKAL4rgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad5fd1cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print a bar chart of the people who were readmitted by age range and a1c level\n",
    "df_ageGroup = copy.deepcopy(df)\n",
    "#replace the 2's with 1's so that the sum works out\n",
    "df_ageGroup.readmitted = df_ageGroup.readmitted.replace(to_replace=2,value=1)\n",
    "df_ageGroup = df_ageGroup.groupby(by=['age','a1c'])\n",
    "readmission_rate = df_ageGroup.readmitted.sum() / df_ageGroup.readmitted.count()\n",
    "readmission_rate.plot(kind='barh', stacked=True, color = ['green', 'red'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar chart of age and A1C level against the likelihood of readmission\n",
    "\n",
    "We can see from the above chart that patients in the 70 to 89 age group were more likely to be readmitted if they had an elevated A1C level.  This also follows intuitively in that older patients are more fragile and an elevated A1C level indicates that they are not controlling their blood sugar levels.  Again, the bar charts allow for easy identification of outstanding attributes.\n",
    "\n",
    "A cross tabulation of the above is shown in the next chart and it shows the number of patients in each group rather than the readmission rate.  The cross tabulation below shows what the simple statistics at the start of this page illustrate:  Most of the patients were in the 60 to 80 age group.  There were very few young patients, and most of the patients had A1C levels in the 7.0 to 8.0 range (of course most of the patients had no A1C measurement, but I imputed the remainder based on the median of the actual measurements)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0xace89aac>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5ExGRbjjzJ3IyVWbMqszbVeigwvdDD5z5ExGRBUMv/irM0VTIqtKDWQeyNic5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa4b7e0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readmission_counts = pd.crosstab([df['age'],df['a1c']], df.readmitted.astype(bool))\n",
    "readmission_counts.plot(kind='bar', stacked=True, color=['green','red'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0xabdd75cc>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XOirfcb1dyQh6jWT06NH4+fkxbdq022oEuH//+98mOe62bdt45plnjBr6crdP\n9oQQt7O21z1YfSeSK3thEjI2vhBC1J7MZy+EEEKIKkljL4SJyVzdBpILA8mFgeSi/m7Y2JeUlGhP\njgshhBDi9lRjn/2jjz6qDdUqxK0iffZCCFF7df3urLGxf+2117h27RqjRo2iVatW2no3N7faRynE\n76SxF0KI2muwxt7X17fKn5hFRUXV+mBClJPG3qDimN13OsmFgeTCQHJh0GC/s5cHI4QQQojbW41X\n9pmZmfz1r3/l3LlzfPPNNxw7doy9e/fywgsvNFaMogmSK3shhKi9On931jRTjr+/v1qzZo1ydHRU\nSilVVFSkevXqVadZd8xNQUGB8vHxUaWlpVXOYPfuu++quXPnKqWU+tvf/qa+//77Ronrz3/+szp2\n7Fit35eenq6srKyUi4uLcnFxUa6urqqoqKgBIizTqlWrG24vKChQ/fv3VyUlJZW28ftMVLLIIoss\nt3Kxvte6ob7yzALUbda7Gm/jZ2dnM2rUKD744AMA7r777iqHPr0drV69moCAgGqHva24ftasWY0V\nFsuWLavze7t3705iYuItjKZ6NQ0X3Lx5c/r378/mzZurHv8/rGHiuu2kA7amDsJMSC4MJBcGtcjF\nlbArDRrK7arGQXVat27NL7/8opX37dvHvffe26BBNZaIiAiCgoKq3a4q3CoJCQlh48aNALz11lv0\n6tULZ2dnpk2bpm1/+eWXefTRR7G3t2f79u0AZGRk4OPjg7u7O+7u7uzduxcwPHAycuRIHBwc+NOf\n/qQdy9fXl4SEBAC++eYb3N3dcXFxwc/Pr06fMzIykr59++Lu7k5wcDB5eXkA2NjYMHPmTFxdXend\nuzcHDx5kyJAhdO/enaVLlwKQm5uLn58f7u7uODk5sWXLliqPMWfOHDw8PHB2diYsLExb/+STTxIR\nEVGnuIUQQtwaNV6iz5s3j8DAQE6dOkXfvn25ePEiGzZsaIzYGlRJSQlJSUnY2dlp69LS0nB1ddXK\nmZmZvPnmm4BhOtdffvmFzZs3k5ycDJTN9V6+/cyZM8THx5OamsrAgQNJTU2lQ4cOfPfddzRv3pyT\nJ08yevRobdyCQ4cOcezYMTp16kS/fv2IjY2lb9++2rEuXrzIhAkT2L17N127duXXX3+t8XNV/Aze\n3t6EhYXx/vvvs2PHDlq0aMG//vUv/u///o933nkHnU5H165dSUxMZOrUqYSEhLB3717y8/PR6/W8\n9NJLtGht2HeVAAAgAElEQVTRgk2bNmFtbU12djZeXl48+eSTRseMjIwkNTWVuLg4SktLCQoKYvfu\n3fTv3x8XFxdiY2Pr8S91B5CrNwPJhYHkwkByUW81Nvbu7u7ExMSQkpICgL29PXfffXeDB9bQsrOz\nsb5uJqRu3boZ3QKfNWtWpQch2rRpg5WVFS+88AIBAQEEBARo24KDg4GyW+kPP/wwKSkpdO3aldDQ\nUA4fPoylpSUnT57U6nt4eNC5c2cAXFxcyMjIoG/fvkDZXYV9+/bh4+ND165dtWPX5PrPsG3bNo4d\nO6btt6ioSHsNaA23o6MjeXl5tGrVilatWtG8eXNycnJo0aIFM2bMYPfu3VhYWPDzzz9z4cIFHnjg\nAW0fkZGRREZGaicZeXl5pKam0r9/f5o3b05paSkFBQVYWVnVGL8QQohbr9rGfuPGjdpTfxX7Zk+c\nOAHQKHOwN7TrG/KqVPzsSiksLS2Ji4tjx44dbNiwgSVLlrBjx45q3z9//nw6derEqlWrKCkpMWrw\nmjdvrr22tLSkuLi42mPXx+DBg/niiy+q3FYeg4WFBc2aNdPWW1hYcO3aNb788kuys7M5ePAglpaW\n2NraUlBQUGk/M2bMYMKECVUe4/q/Ic0moPz8xQroiOEMPv33/94J5fLX5hKPKcvl68wlHlOWMwEv\nM4rHlOW91Or7ofwn4+W/zb+dy9HR0YSHhwNlXa91VW1jv3XrVnQ6HRcuXCA2NpbHHnsMKBtMp2/f\nvrd9Y9++fXtyc3NrrHf9CUFeXh55eXkMGzaMvn370q1bN63e+vXrGTduHGlpaZw6dQp7e3tycnJ4\n6KGHAPjss88oKSm5qfh0Oh19+vRh4sSJZGRkYGNjw6VLl7jvvvuIi4vjP//5DytXrqxxP3369GHS\npEmkpaXRrVs38vLy+Pnnn3nkkUdu+DnL5eTk8MADD2BpaUlUVBSnT5+uVMff35933nmHMWPG0KpV\nK86dO0ezZs24//77KSwsxNLS0ujERvPUDQK//radlO+M8vVf4qaOR8rmUe543boa6l8/AM/tXPb1\n9TUq1/Vh8Wob+/IzicGDB2v9ygDnz59n3LhxdTqYObG0tESv15OSkoK9vT1Q9ZV0xXU6nY4rV64Q\nFBREQUEBSiltoiCdTscf/vAHPDw8yMnJYenSpTRv3pyJEyfy9NNP89lnnzF06FBat25d5b6r0r59\nez755BNGjBhBaWkpHTp04Ntvv+XMmTO0bNmyyvdcv8/27dsTHh7OH//4RwoLCwF4//33KzX25c8J\nXF8eM2YMgYGBODk50bt3bxwcHCoda/DgwRw/fhwvr7LLEGtraz7//HPuv/9+EhMTtfWiGtd/cd3J\nJBcGkgsDyUW91TioTo8ePTh+/Lj2xV5aWkrPnj21B9RuZ+Hh4WRlZTF9+vR672v8+PEEBgY2yh2P\nadOmMXbsWPR6fYMfq75mzpzJo48+ylNPGV/G63Q6+emdEOLWC7u5LtrbVV0H1anxp3d+fn74+/sT\nHh7OihUrePzxxxk8eHCdgjQ3o0ePZvv27bfdH8a///3v26KhLyws5IcffmD48OGmDsW8pddc5Y4h\nuTCQXBhILuqtxit7pRSbNm1i165d6HQ6fHx8Kl2lCVFbt+rhQyGEqMj6Xmtyfs0xdRgNpsFmvROi\nIcjY+EIIUXsNdht/48aNPPLII9xzzz1YW1tjbW3NPffcU6cghRBCCNH4amzsp02bxpYtW8jJyeHK\nlStcuXJFGzVOCFF/Mo20geTCQHJhILmovxob+44dOxr93EoIIYQQt5ca++ynTJlCZmYmw4cP10ZY\n0+l0t/2gOsK0pM9eCCFqr67fnTWOjf/bb7/RokULIiMjjdZLYy+EEELcHuRpfGEScmVvUD7dsZBc\nVCS5MJBcGDTY0/gpKSkMGjSIXr16AfDjjz8ye/bs2kcohBBCCJOo8crex8eHOXPm8PLLL5OYmIhS\nCr1ez9GjRxsrRtEEyZW9EELUXoNd2V+9ehVPT0+jAzWF+eyrUlhYyIABA7REnjhxgscffxw7Ozvc\n3d0ZNWoUFy5cMFl8TzzxxC3/2WN0dDQWFhZ8+umn2rpDhw5hYWHBvHnzbvjesLCwGussWrSIVatW\nVbmtfLIdWWSRRZbbdbnvNhl3psbG/v777yc1NVUrb9iwQZsBr6lZvXo1AQEB6HQ6CgoKCAgIYNKk\nSZw4cYKEhAQmTpzIxYsXTRbf9u3bb/mARjqdDr1ez7p167R1ERERODs7o9PdeEjbmrZD2QRBixcv\nrnKbkgUFRJlBDOaySC4kF7dbLi5fucLtoMbGfsmSJbz00kskJyfTuXNn5s+fz0cffdQYsTW6iIgI\ngoKCAPjiiy/o27cvTzzxhLZ9wIAB9OrVi4yMDHx8fHB3d8fd3Z29e/cCZVfJgYGBWv3Q0FBtzvn4\n+Hj69euHi4sLnp6e5ObmVruf8+fP4+Pjg6urK46OjuzZswcAm9/ntAd46qmn6N27N3q9nmXLlmnH\nbN26NW+//TYuLi54eXnd1J2Irl27UlhYyIULF1BK8e233zJs2DDtDseyZcvw8PDAxcWFZ555hvz8\n/Er7SEtLY9iwYfTu3RsfHx9SUlKAsulu27VrJ90+QghhQjU29t26dWPHjh1kZ2eTkpLCnj17sLGx\naYTQGldJSQlJSUnY2dkBcPToUdzd3aus26FDB7777jsSEhJYs2YNr776apX1ym/zFBUV8eyzz7Jo\n0SIOHTrEjh07aNGiRbX7+eKLLxg6dCiJiYkcPnwYZ2dnbX/lli9fzoEDB4iPj2fRokVcvnwZKOt2\n8fLy4tChQ/j4+BidCNzIM888w/r169m7dy9ubm40b95c2/b0008TFxfHoUOHcHBwMLrlXx7ThAkT\nWLx4MQcOHGDOnDlMnDhRq+Ph4cGuXbtuKo47ka+pAzAjvqYOwIz4mjoAM+Jr6gCagBp/Zz9v3rxK\nt2vvvfde3N3dcXFxabDAGlt2djbW1tZG66p7CKKoqIjQ0FAOHz6MpaUlJ0+erHa/SilSUlLo1KmT\ndvLQunXrG+7Hw8OD559/nmvXrjF8+HCtsa9o4cKFbN68GYCffvqJkydP4uHhQbNmzbS7Ee7u7nz3\n3Xc3/Nzln3HkyJEEBweTnJzMH//4R2JjY7U6R44c4e233+a3334jNzeXoUOHGu0jLy+P2NhYRo4c\naZSjcp07d+bUqVM3jEMIIUTDqbGxT0hI4MCBAwQGBqKUYvv27Tg6OvLxxx/zzDPPMH369MaIs1FU\nbNx79epFTExMlfXmz59Pp06dWLVqFSUlJVhZWQFw1113UVpaqtUrKCgAqu/brm4//fv3Z/fu3Wzb\nto2QkBCmTp3Kc889p70vOjqaHTt2sG/fPqysrBg4cKB2rIoPT1pYWFBcXHxTn71Dhw40a9aM77//\nnoULFxIbG6vFHRISwpYtW3B0dGTlypWVxqkuLS2lbdu2JCYmVrlvpVSVOQgBbH5/3QZwwXAGX36E\nO6Fc/tpc4jFluXyducRjyvIh4C9mFI8pywsw8++H378Ty8cCuJXl6OhowsPDAep3V13VwNvbW125\nckUrX7lyRfXv31/l5eWpHj161PT220ZxcbHq2LGjVs7Pz1fdu3dX27dv19bFxMSopKQk9dprr6l5\n8+YppZRavny50ul0Simlzpw5o2xsbFRhYaG6fPmysrW1VStXrlRFRUXq4YcfVvHx8UoppXJyclRx\ncXG1+zl9+rQqLi5WSim1ePFi9dprrymllLKxsVG//PKL+uqrr1RgYKBSSqnjx48rKysrFRMTo5RS\nqnXr1lq869evVyEhIUoppb788ks1Y8aMSp87KipKBQQEKKWUio2NVV999ZVSSql3331XzZ07Vyml\nVPv27dWFCxdUUVGR8vPzU+PHj69Up2/fvmr9+vVKKaVKS0vVoUOHtGP87W9/Ux999JHRcQGlZFEK\nVJQZxGAui+RCcnG75eImmtFbqq7Hq7HP/uLFi9qY+FB25ZiVlUXLli21K9GmwNLSEr1erz1YZmVl\nxbZt21i8eDF2dnb06tWLjz/+mAceeICJEyeycuVKXFxcSElJ0W7Ld+nSheDgYPR6PaNGjcLNzQ0o\ny9natWuZPHkyLi4u+Pv7U1hYWO1+oqKicHFxwc3NjfXr1zNlyhSjWIcOHUpxcTE9e/ZkxowZeHl5\nadsqXkGXPzMAZQ/Q3XvvvZU+d8U6Xl5ePPnkk5XWv/fee3h6euLt7W00KVLFOqtXr+bTTz/FxcUF\nvV7P1q1btXpxcXH079+/1v8mdwpfUwdgRnxNHYAZ8TV1AGbE19QBNAE1Dqrz3nvv8eWXXzJ8+HCU\nUmzdupUnn3ySN954gwkTJrB69erGirXBhYeHk5WV1aS6Jso999xzLFiwgHbt2jXqcXNychg0aBDx\n8fFG63U6HTf8wxNCiNuADqihGb21x9PVbVCdmxobPz4+nj179qDT6ejXrx+9e/euU5DmrqioCD8/\nP2JiYm7qN+SiZosWLeK+++7jT3/6k9F6aewNopErl3LRSC7KRSO5KBeN+eaiSTX2QtxqcjIlhGgK\n2lpbc+kWj2x6I3Vt7Gt8Gv96PXr0AMoGjAkNDa31AYUoJ+eZQgjROGrd2CcnJ5Odnc3+/fsbIh4h\nhBBC3GI1Po0PkJGRwffffw+UjdDWvHlzo2FkhRB1d/24BXcyyYWB5MJAclF/NTb2n3zyCSNHjuSl\nl14C4OzZswwfPrzBAxNCCCHErVHjA3rOzs7ExcXRp08fbYQ0R0dHjhw50igBiqaprg+ZCCHEnayu\n3501Xtk3b97caFKU4uJieZJaCCGEuI3U2NgPGDCA999/n6tXr/Ldd98xcuRIo2lchRD1I/2RBpIL\nA8mFgeSi/mps7D/44APuv/9+HB0dWbp0KY8//jizZ89ujNiEEEIIcQvU2Gefl5eHlZUVlpaWQNm8\n74WFhbRs2bJRAhRNk/TZCyFE7dX5u7OmmXI8PDyMZr3LyclRXl5edZp1pzEVFBQoHx8fVVpaqpRS\nKiUlRQ0bNkw98sgjys3NTQUHB6usrCyTxff444+r33777ZbuMyoqSt1zzz3KxcVFubi4qMGDB9/S\n/VeUnp6u9Hr9DetkZmaqYcOGVbmN32eLkkUWWWS5Uxfre61r/d0LdZv1rsZBdQoLC7XZ2ACsra25\nevVqTW8zudWrVxMQEIBOp6OgoICAgADmz5+vjQ8QExPDxYsXeeCBB0wS3/bt2xtkvwMGDGDLli0N\nsu/a6tChA23btuXgwYPaDIBGwho9JPOUDtiaOggzIbkwkFwYNNFcXAm70mjHqrHPvlWrViQkJGjl\nAwcO0KJFiwYN6laIiIggKCgIgC+++IK+ffsaDQQ0YMAAevXqRUZGBj4+Pri7u+Pu7s7evXuBsgdC\nKj6IGBoaysqVK4GyiYH69euHi4sLnp6e5ObmVruf8+fP4+Pjg6urK46OjuzZswcAGxsbLl26BMBT\nTz1F79690ev1LFu2TDtm69atefvtt3FxccHLy4sLFy7U+LlVFbd3Pv/8czw9PXF1deXll1+mtLRU\n2/+0adPQ6/UMHjyYffv2MWDAALp166ZNUVvd56qopKSEN998Ew8PD5ydnfnkk0+0bU8++SQRERE1\nxi2EEKLh1NjYL1iwgODgYLy9vfH29mbUqFEsXry4MWKrs5KSEpKSkrCzswPg6NGjuLu7V1m3Q4cO\nfPfddyQkJLBmzRpeffXVKuuVz91eVFTEs88+y6JFizh06BA7duygRYsW1e7niy++YOjQoSQmJnL4\n8GGcnZ21/ZVbvnw5Bw4cID4+nkWLFnH58mWgbLRCLy8vDh06hI+Pj9GJQHV2796Nq6srrq6u/POf\n/+T48eOsW7eO2NhYEhMTsbCw0KYlvnr1KoMGDSIpKQlra2v+9re/sXPnTjZt2sTf/va3m87Pp59+\nSps2bYiLiyMuLo5ly5aRkZEBgIeHB7t27aox7jtaE7xiqTPJhYHkwkByUW813sZ/9NFHOX78OCkp\nKeh0Ouzt7bn77rsbI7Y6y87Oxtra2mhdVVe8UDatbWhoKIcPH8bS0pKTJ09Wu1+lFCkpKXTq1Ek7\neSjv4qhuPx4eHjz//PNcu3aN4cOHa419RQsXLmTz5s0A/PTTT5w8eRIPDw+aNWum3Y1wd3fnu+++\nq/Gz9+/fX7sqB1iyZAkJCQnatMT5+fl07NgRgGbNmuHv7w+UDZRU/iCmXq/XGuvrP9eJEycqHTMy\nMpIjR46wYcMGoGwO+9TUVGxsbOjUqZO2LyGEEKZxUxPhpKSkcOzYMQoKCjh48CAAY8eObdDA6qti\n496rVy9iYmKqrDd//nw6derEqlWrKCkpwcrKCoC77rpLu90NUFBQAFQ/NWt1++nfvz+7d+9m27Zt\nhISEMHXqVJ577jntfdHR0ezYsYN9+/ZhZWXFwIEDtWNVPKmysLCguLi4Lqlg3Lhx/OMf/6i0/vr9\nN2vWrNKxqvtc11uyZAmDBw+utF4pVf0gTJuANr+/tgI6YjiDT//9v3dCufy1ucRjynL5OnOJx5Tl\nTMDLjOIxZXkvTfP74Xfl4wj4+vpWKkdHRxMeHg6Udf/WVY2NfVhYGDExMRw9epQnnniC//3vf3h7\ne5t1Y9++fXtyc3O18ujRo/nnP//J119/zeOPPw7Arl27aNeuHTk5OTz00EMAfPbZZ5SUlADQtWtX\njh07RlFREVevXmXHjh30798fe3t7zp8/z4EDB+jduzdXrlyhZcuW1e7nzJkzPPjgg/z5z3+moKCA\nxMREo8Y+JyeHtm3bYmVlRXJyMvv27avx823atIn4+PgqG/DrDRo0iKCgIF577TXuv/9+Ll26RG5u\nLn/4wx9uKpfVfa6K/P39+fDDDxk4cCB33XUXJ06c4KGHHqJly5acP3+erl27Vr3zp25w4Otv20n5\nzihf/6Vo6nikbB7ljtetM3U8t7hc3shXVfb19TUqz5o1i7qosc9+w4YNfP/993Tq1IkVK1Zw+PBh\nfv311zodrLGU34pOSUkBwMrKim3btrF48WLs7Ozo1asXH3/8MQ888AATJ05k5cqVuLi4kJKSot2W\n79KlC8HBwej1ekaNGqU9TX733Xezdu1aJk+ejIuLC/7+/hQWFla7n6ioKFxcXHBzc2P9+vVMmTLF\nKNahQ4dSXFxMz549mTFjBl5eXtq2ilfE5c8MAKSlpXHvvfdW+twV65RzcHBg9uzZDBkyBGdnZ4YM\nGUJmZmal/Vd1PKDaz1Wxzp///Gd69uyJm5sbjo6OvPLKK9pJQVxcHD4+Pjf41xKVvgjuZJILA8mF\ngeSi3mocVOfRRx8lPj4ed3d3du7cyT333EOPHj20htRchYeHk5WVxfTp000dyi333HPPsWDBAtq1\na2fqUGo0ZswY3njjDVxdXY3W63Q6+emdEOLOFlb982TVqeugOje8ja+UwtHRkcuXL/Piiy/Su3dv\nWrVqRd++fWt9oMY2evRo/Pz8mDZtWqUr2NvdqlWrTB3CTblw4QK//vprpYZeE9ao4QghhFmxvte6\n5kq3yA2v7Msb+6SkJADS09PJycmp8olyIWpDhss1iI6OrtRnd6eSXBhILgwkFwZ1/e6s8Tb+uHHj\nmDRpEh4eHnUOTojrSWMvhBC112CNvb29PampqXTt2pVWrVppB/vxxx/rFqkQSGMvhBB10WCNfXUD\notTn935CSGNvILcoDSQXBpILA8mFQYM8oAfSqAshhBC3uxqv7IVoCHJlL4QQtVfX784aB9URQggh\nxO1NGnshTKx8HGwhuahIcmEguag/aeyFEEKIJk767IVJNLVRDYVoqtpaW3MpJ8fUYYjfSZ/9baCw\nsJABAwYY/UMtWLCAFi1akHMT/zP169fvlscUHh6OhYUFO3bs0NZt3rwZCwsLvvzyyxu+NyQkhI0b\nN96wztSpU9m9e3eV25Qssshi9svlK1cQtz9p7BvR6tWrCQgIMLqqjYiIYPDgwTU2rAB79uy55THp\ndDocHR1Zs2aNUUwuLi439d6artBfeeUV5syZU+84m7JoUwdgRqJNHYAZiTZ1AGZE+uzrTxr7RhQR\nEUFQUJBWTktL49q1a8ycOZOIiAht/dGjR/H09MTV1RVnZ2fS0tIAtOllc3Nz8fPzw93dHScnJ7Zs\n2QKUDYDk4ODAhAkT0Ov1+Pv7U1BQUGNc/fv3Jy4ujuLiYnJzc0lLS8PZ2Vm7A/H3v/8dDw8PHB0d\neemll4zeW14nISEBX19fevfuzdChQ7VpdB955BEyMjLMflpkIYRoyqSxbyQlJSUkJSVhZ2enrVuz\nZg3BwcH06dOH1NRULl68CMDSpUuZMmUKiYmJJCQk8OCDDwKGfu4WLVqwadMmEhIS2LlzJ6+//rq2\nz9TUVEJDQ0lKSqJNmzY13mYv3+/gwYP59ttv2bJlC08++aTR9smTJxMXF8eRI0fIz89n27ZtRu+9\ndu0akydPZuPGjRw4cIDx48fz17/+Vavj6urK3r1765C1O4OvqQMwI76mDsCM+Jo6ADMio+fVX40j\n6IlbIzs7G2tr4+kM16xZw+bNmwEYPnw469atY9KkSXh5efH+++9z9uxZRowYQffu3Y3eV1payowZ\nM9i9ezcWFhb8/PPPXLhwAQBbW1ucnJwAcHd3r3a44+uNGjWKhQsXkpOTw7x58/jHP/6hbdu5cydz\n5szh6tWrXLp0Cb1eT0BAAFB2ZZ+SksLRo0fx8/MDyk5sOnfurL2/c+fOVcYRAtj8/roN4ILhCy76\n9/9KWcpSNoPy77fRyxtdKTdeOTo6mvDwcKCeI9oq0SgyMzNV9+7dtfKPP/6omjdvrmxsbJSNjY3q\n3Lmz6tevn7b91KlTatGiReqRRx5RO3fuVEop1bp1a6WUUitWrFCjRo1SxcXFSimlbGxs1OnTp1V6\nerrS6/XaPubOnavCwsJuGFd4eLgKDQ1VSinl7Oys+vbtq5RSKiQkRG3cuFHl5+erDh06qLNnzyql\nlAoLC1OzZs3S6mzYsEEdOXJEeXl5VXuM6dOnq48++shoHaCULEqBijKDGMxlkVyYXy7MoZmIiooy\ndQhmo67/HnIbv5G0b9+e3NxcrRwREcGsWbNIT08nPT2dc+fO8fPPP3PmzBnS09OxtbVl8uTJBAUF\nceTIEaN95eTk8MADD2BpaUlUVBSnT5+u8fhLlizhP//5T6X1ZX87ZT744AOjK3pA6/Nv164dubm5\nrF+/3mi7TqfD3t6eixcvsm/fPgCuXbvGsWPHtDrnz5+XORaEEMKE5DZ+I7G0tESv15OSkoK9vT1r\n167lf//7n1Gdp556ijVr1qCUYtWqVdx999106tRJ6/8u77MfM2YMgYGBODk50bt3bxwcHLR9XP90\nfHk5OTmZ/v37V4qr4hP1Q4cOrbS9TZs2vPjii+j1ejp27Iinp2elOnfffTcbNmzg1Vdf5bfffqO4\nuJjXXnuNnj17ApCYmMiiRYtuOld3Gl9TB2BGfE0dgBnxNXUAZkT67OtPBtVpROHh4WRlZTF9+vRG\nP3ZgYCCbNm3irrsa9/zuxIkTvPHGG9ovBsrpdDrkD08I86fD+A6gMK0Gm89e3DpFRUX4+fkRExNz\nx4wgN3XqVEaMGIG3t7fR+jvl8wtxuzOHEfRkPnsDaezFbUWmuDWQLzIDyYWB5MJAcmEgjb24rUhj\nL4QQtSdj4wshhBCiStLYC2FiMu63geTCQHJhILmoP2nshRBCiCZO+uyFSUifvRBC1J702QshhBCi\nStLYC2Fi0h9pILkwkFwYSC7qTxp7IYQQoomTPnthEjKCnhC3H+t7rcn51bSj6d3pZFCdRlZYWMiQ\nIUOIjo7WGq4FCxYwY8YMsrKyuOeee274/n79+rFnz55bGlN4eDhvvvkmDz30EADOzs7aPMi3WnR0\nNPPmzWPr1q3V1vnxxx9ZuHAhn376aaVtOp0OwhokNCFEQwmTcfJNTR7Qa2SrV68mICDA6Ao1IiKC\nwYMH8+WXX9b4/lvd0EPZH8Ef//hHEhMTSUxMbLCG/mY5OTmRlpbGhQsXTBqH2Us3dQBmRHJhILnQ\nSJ99/UljX0cREREEBQVp5bS0NK5du8bMmTOJiIjQ1h89ehRPT09cXV1xdnYmLS0NgNatWwOQm5uL\nn58f7u7uODk5abPDZWRk4ODgwIQJE9Dr9fj7+2tzy99IVWd8c+bMwcPDA2dnZ8LCwrT99+jRg/Hj\nx2Nvb8+YMWOIjIykX79+2NnZER8fD0BcXBx9+/bFzc2Nfv36ceLEiUr7z8vL4/nnn8fT0xM3Nzej\nGe6GDRvG+vXra4xbCCFEw5HGvg5KSkpISkrCzs5OW7dmzRqCg4Pp06cPqampXLx4EYClS5cyZcoU\nEhMTSUhI4MEHHwQMfdYtWrRg06ZNJCQksHPnTl5//XVtn6mpqYSGhpKUlESbNm3YuHHjDeNSSrF2\n7VpcXV1xdXUlPDycyMhIUlNTiYuL02LYvXs3UHaC8sYbb5CcnExKSgpr165lz549zJ07l3/84x8A\nODg4sHv3bg4ePMisWbOYOXNmpeO+//77DBo0iP3797Nz507efPNN8vPzAfDw8GDXrl11TfWdwdbU\nAZgRyYWB5EIjk+DUX+NObt5EZGdnY21tbbRuzZo1bN68GYDhw4ezbt06Jk2ahJeXF++//z5nz55l\nxIgRdO/e3eh9paWlzJgxg927d2NhYcHPP/+s3fa2tbXFyckJAHd3dzIyMm4Yl06n49lnn2XRokXa\nujfeeIPIyEhcXV2Bsqvw1NRUunTpgq2tLb169QKgV69e+Pn5AaDX67Vj/frrr4wdO5bU1FR0Oh3X\nrl2rdNzIyEi2bt3K3LlzgbLnGc6cOYO9vT2dOnWqPu5NQJvfX1sBHTF8wZXfwpSylKVsPuXfld9W\nL2+Epdxw5ejoaK1L1sbGhrqSB/TqICsrC29vb06ePAnAkSNHePTRR+nUqRNQNm+9ra0tP/zwAwDp\n6VqYp+0AABjWSURBVOls27aNxYsXs3TpUgYOHIi1tTVXrlwhPDycb775htWrV2NpaYmtrS0xMTGU\nlpYSGBjIkSNHAJg3bx65ubm8++671ca1cuVKDhw4wOLFi7V1b7zxBnZ2dkyYMMGobkZGhtH+x48f\nT0BAAE8//bTRtpCQEHr37k1oaCinT5/G19eX9PR0owf0evfuTUREBI888kilmI4fP8748ePZt2+f\n0Xp5QK+CdOQqrpzkwsAccxFmmgf0ZIpbA3lArxG1b9+e3NxcrRwREcGsWbNIT08nPT2dc+fO8fPP\nP3PmzBnS09OxtbVl8uTJBAUFaY1ruZycHB544AEsLS2Jiori9OnTNR5/yZIl/Oc//6m0vqo/AH9/\nf5YvX05eXh4A586d07oYbkZOTg6dO3cGYMWKFVXW8ff3N7qbkJiYqL0+f/48Xbt2venjCSGEuPWk\nsa8DS0tL9Ho9KSkpAKxdu5annnrKqM5TTz3FmjVrWLduHXq9HldXV44ePcrYsWMBQ5/9mDFjOHDg\nAE5OTqxatQoHBwdtH9f/Fr28nJycTPv27SvFpdPpKr1n8ODBjB49Gi8vL5ycnAgODtZOVKrbf8XX\n06ZNY8aMGbi5uVFSUlJlnXfeeYdr167h5OSEXq83uvsQFxeHj49P1YkUZczt6s2UJBcGkguNXNXX\nn9zGr6Pw8HCysrKYPn16ox87MDCQTZs2cddd5v/Iha+vL+vWreOBBx4wWi+38YW4DYXJ7+xNTQbV\naWRFRUX4+fkRExMjo8FV48cff2TRokX897//rbRNcibE7cdUI+hJn72BNPbitiJT3BrIF5mB5MJA\ncmEguTCQxl7cVqSxF0KI2pOn8YUQQghRJWnshTAxGffbQHJhILkwkFzUnzT2QgghRBMnffbCJKTP\nXgghak/67IUQQghRJWnshTAx6Y80kFwYSC4MJBf1J429EEII0cSZ/3irosmSUfSEELebttbWXMpp\n/FEE68vsruwLCwsZMGCA0QMICxYsoEWLFuRUSHBycjIuLi64u7uTnp5eaT9PPPGEUf1bZfPmzVhY\nWGiT4DSEhIQEpkyZ0mD7rygjIwMLCwveeecdbV12djZ33303kydPvuF7w8PDa6yzZcsW3nvvvSq3\nKVlkkUWW22y5fOUKtyOza+xXr15NQECA0VVfREQEgwcP5ssvv9TWbd68mZEjR5KQkICtrWF6KKUU\nSim2b9/OPffcc8vji4iIICAggIiIiFu+b4Di4mLc3d1ZuHBhg+y/Kra2tnz99ddaef369ej1+hqv\nvG/myjwwMJCNGzdy7dq1esfZVEWbOgAzEm3qAMxItKkDMCPRpg6gCTC7xj4iIoKgoCCtnJaWxrVr\n15g5c6bWwH799dcsXLiQjz76iEGDBnH69Gns7e0ZN24cjo6O/PTTT9jY2HDp0iUAPvvsM5ydnXFx\ncWHcuHEAbN26lT59+uDm5sbgwYO5cOECAGFhYTz//PMMHDiQbt26sXjxYi2W3Nxc9u/fz5IlS1i7\ndq22Pjo6mgEDBjB8+HC6devGW2+9xapVq/Dw8OD/27v3oKjKNw7g3wUJZCNFQYtKVkCTWvfGRVFR\nTJHENDDFypGw7KbdTLMSfyOWThc1EzXHnIyyXDAMLyUOE+4ylukKKkhoha5AKigIIRTg4vP7g/bA\nCsiq7cX1+cyccd+z57zvcx6Xfc/lhVcmk+HUqVMAgAsXLmDKlCkIDQ1FaGgo9u/fL7Q5Y8YMjBgx\nAvHx8cjJycHEiROFNmfOnAmZTAa5XI6MjAwAwOzZsxESEgKpVIqkpCQhFolEgqSkJAQFBUEmk5l1\nB8Ld3R2BgYHIy8sDAGzduhVxcXHC3ZXOctVWZ8cmEokQFhaGrKysLuNgjDFmIWRHDAYD3X333Sbr\nli5dSu+//z4REfn5+dH58+eJiCgpKYlWrlxJRER6vZ6cnJzo4MGDwn4SiYSqqqqosLCQBg4cSFVV\nVUREdPHiRSIiqq6uFrbduHEjzZs3j4iIFi9eTMOHD6empiaqrKyk3r17k8FgICKir7/+ml544QUi\nIgoPD6e8vDwiItJoNNSzZ08qLy+nxsZG8vHxocWLFxMR0erVq+n1118nIqInn3ySfvrpJyIiKikp\nocDAQKHN4OBgamhoEOp79NFHiYhowYIFNHfuXCFWY9zG4zAYDBQREUHHjh0Tjnvt2rVERPTpp5/S\nrFmzrplzvV5PUqmUdu3aRfPnz6eysjIaM2YMpaSk0Msvv3zNXH3xxRfCNp0dGxHRpk2baMGCBSbt\nAiDihRdeeLnFFlt3mzfavl0N0KusrISHh4fJutTUVGzfvh0AEBMTg61bt2LOnDkAgJbjbuHr64vQ\n0FCTfYkIe/fuRVxcHHr16gUA8PT0BACUlZUhLi4O5eXlaGpqgp+fH4CWK9EJEybAxcUFvXv3Rp8+\nfVBRUQEfHx+o1WrMnTsXADB16lSo1WqoVCoAQEhICPr27QsACAgIQFRUFABAKpVCo9EAAH788Ucc\nP35ciO/SpUuor6+HSCTCpEmT4Orq2i4n2dnZJncRevbsCQBIS0vDxo0bYTAYcO7cORQVFUEqlQIA\nJk+eDABQqVQmjz6uJSoqCosWLULfvn0xbdo0k/c6y1VbHR3b33//DXd3d/j4+GDPnj3t9kkAIDEe\nFwAFgIh/y9p//+Uyl7nMZbsr//urgMaZ+CxZ1mq1SElJAdBy5/aG/aenHDepvLycAgIChHJBQQG5\nurqSRCIhiURCPj4+NHz4cCJqubJfsWIFEbVenbYlkUiosrKS1qxZQ4mJie3aGjVqFO3atYuIiLRa\nLUVERLSrl4hIKpVSSUkJVVVVkbu7O/n6+pJEIqH777+f+vXrR0SmV+JERBERESZX/cb3vLy8qLGx\nsV0sV7fZdp+goCD6448/TLY/deoUBQQEUE1NDRERJSQk0Jdffikct/EuxqFDh4Tj6kzb3D3zzDN0\nzz33UHV1tclVe2e5artNZ8dGRLR7926aNm2ayTrYwRm6vSwaO4jBXhbOBefC3nNh627zRtu3q2f2\nXl5eqKurE8pqtRpLliyBXq+HXq/HmTNncPbsWZSWlppVn0gkwsMPP4xvv/1WeH5fXV0NAKitrYWP\njw8ACGdNANCSS1NEhPT0dMTHx+P06dPQ6/UoLS1F//79sW/fPrOPb9y4cUhOThbK+fn5Xe4TGRmJ\ndevWCeWamhrU1tZCLBbjrrvuQkVFBTIzM7usR6fTCeMVOjNv3jx8+OGHwt0Do85y1dbVx3b06FHh\n9blz5+Dr69tljIwxxizDrjp7Z2dnSKVSYVBZWloaYmNjTbaJjY1FamoqANPR4FePDDeWH3zwQSQm\nJmLUqFFQKBSYN28egJZBcVOnTkVwcDC8vb2F7UUiUYf1pqamtovl8ccfh1qtbrfP1XEY30tOTkZu\nbi7kcjkeeughbNiwocP42+6zaNEiVFdXY/DgwVAoFNBqtZDL5VAqlRg0aBCmT5+OESNGdNl2aWkp\n3N3dO93OmKsZM2a029ecXF19bJ999plQv06nw8iRIztsm7XeGmSci7YibB2AHYmwdQAOwO4mwklJ\nSUFFRQXeeustW4fiUBYsWID4+Hjhub61XLlyBSqVCrm5uejWrXWIiEgkgl198BhjzAwidHwH2Grt\n3+BEOHbX2Tc1NWHs2LHIycnhv7DmAHbu3ImCggIsWrTIZD3/3zLGbkW2/gt6DtPZs9sDT3HbSqvV\nCqNwb3eci1aci1aci1bc2bNbCnf2jDF2/Xg+e8YYY4x1iDt7xmyM5+puxbloxbloxbm4edzZM8YY\nYw6On9kzm+Bn9owxdv34mT1jjDHGOsSdPWM2xs8jW3EuWnEuWnEubh539owxxpiju/G5d7rW0NBA\nI0eOpCtXrgjrVq1aRW5ubvTXX38J644fP05yuZxUKhWdOnWqXT3R0dEm2/9XMjIySCQS0YkTJ/7z\nuo1yc3Pp1VdftVj9ben1enJzcyOFQkEKhYKUSiU1NTVZrD2xWHzN9xsaGig8PJyam5vbvYd/Z4/i\nhRdeeHGExaOHh6W+att9d94Iiw7Q27RpE6qqqvDmm28K64YMGYK+ffti8uTJSEhIAAB88MEHaG5u\nRmJiosn+xtAs9adVp02bhn/++QcqlQpJSUn/ef0Gg8Hk78Fb2unTpzFx4kQcO3bMKu15eHjg0qVL\n19wmMTERQUFBmDx5ssl6kUgEJFkwOMYYs6YkWGXQsV0O0FOr1XjssceE8smTJ3H58mUsXLgQarUa\nALB7926sXr0a69evx5gxY1BSUoIHHngATz/9NAYPHoyysjJIJBJhitqvvvoKcrkcCoVCmLJ1165d\nGDp0KFQqFSIjI3H+/HkALbO1PfPMMxg9ejT8/f2xZs0aIZa6ujocPHgQa9euRVpamrBeq9Vi1KhR\niImJgb+/P95++21s3rwZoaGhkMlkOHXqFADgwoULmDJlCkJDQxEaGor9+/cLbc6YMQMjRoxAfHw8\ncnJyMHHiRKHNmTNnQiaTQS6XIyMjAwAwe/ZshISEQCqVmpx0SCQSJCUlISgoCDKZTJgN8HplZWVh\n2LBhCAoKQlxcHOrr64X6Fy5cCKVSieDgYBw+fBjjxo1DQECAMCNfXV0dxo4dK8Swc+fODttYvnw5\nQkNDIZfLTY5h0qRJwv8164Te1gHYEc5FK85FK87FTbNYZ9/c3IzCwkIMHDhQWJeamoq4uDgMHToU\nxcXFuHDhAqKjo/Hiiy/ijTfeQHZ2NogIxcXFmDNnDgoLC9GvXz/hyv7XX3/FsmXLoNFocPToUXzy\nyScAgPDwcBw4cACHDx/GtGnT8NFHHwlt/v7778jKyoJOp8OSJUvQ3NwMANixYwceeeQR9OvXD97e\n3jh8+LCwT0FBATZs2IDjx49j8+bNOHnyJHQ6HWbNmiWcMLz22muYO3cudDod0tPTMWvWLGH/EydO\nIDs7G1u2bDE5A3vvvffg6emJgoIC5OfnY/To0QCAZcuW4dChQ8jPz0dOTg4KCwsBtJzBeXt7Iy8v\nDy+99BJWrFjRZd5PnjwJpVIJpVKJV155BVVVVVi2bBmys7ORl5eHoKAgfPzxx0L9vr6+OHLkCEaO\nHImEhARkZGTgwIEDWLx4MQCge/fuyMjIQF5eHvbu3StMEdxWVlYWiouLodPpcOTIEeTl5WHfvn0A\nAIVCIZwIMcYYsw2L3WOurKyEh4eHybrU1FRs374dABATE4OtW7dizpw5AExvf/j6+iI0NNRkXyLC\n3r17ERcXh169egEAPD09AQBlZWWIi4tDeXk5mpqa4OfnB6ClM5swYQJcXFzQu3dv9OnTBxUVFfDx\n8YFarcbcuXMBAFOnToVarYZKpQIAhISEoG/fvgCAgIAAREVFAQCkUik0Gg0A4Mcff8Tx48eF+C5d\nuoT6+nqIRCJMmjQJrq6u7XKSnZ1tchehZ8+eAIC0tDRs3LgRBoMB586dQ1FRkTAVrfH2t0qlwnff\nfddl3v39/XHkyBGh/P3336OoqAjDhg0D0DKroPE10HLlDQCDBw9GfX09xGIxxGIxXF1dUVtbi+7d\nu+Odd97Bvn374OTkhLNnz+L8+fPo06ePUEdWVhaysrKgVCoBAPX19SguLkZ4eDhcXV1x5coVNDQ0\nwM3Nrcv4b0v9bR2AHeFctOJctOJc3DSLPlBu24EfO3YMf/zxB8aOHQugpdPp37+/0Nm3JRaLO6yv\ns2cVr7zyCubPn49HH30UOTk5JreR77jjDuG1s7MzDAYDLl68CI1Gg8LCQohEIjQ3N0MkEmH58uUA\nYNJROzk5CWUnJycYDAbh2A4ePGhSv5G7u7tZOQEAvV6PlStXIjc3Fz169MDMmTPR0NAgvG9s2xj7\njYiMjMSWLVs6fK/tsbU9FicnJ1y+fBnfffcdKisrcfjwYTg7O6N///4m8Rm98847eP755ztsg4g6\nHneRAaDnv6/dANyN1h9q4207LnOZy1y+Vcr/Mv6qoHGmvpspa7VapKSkAGh59HqjLNbZe3l5oa6u\nTiir1WosWbIEb731lrDOz88PpaWlZtUnEonw8MMPIzY2Fm+88QZ69eqF6upqeHp6ora2Fj4+PgAg\nJAXoeLAEESE9PR3x8fFYv369sD4iIkK49WyOcePGITk5GfPnzwcA5OfnQy6XX3OfyMhIrFu3DqtW\nrQIA1NTUoLa2FmKxGHfddRcqKiqQmZkp3N7vjE6nw7p16/Dll192GefQoUMxZ84cnDx5Ev7+/qiv\nr8fZs2cxYMAAk+06G/BRW1uLPn36wNnZGRqNBiUlJe22iYqKwv/+9z9Mnz4dYrEYZ86cwR133AFv\nb280NjbC2dm5wzsdiL1G4FefyTtyWW9n8diybMyFvcRjy7K+i/dvp/LV62wdTxflq6fjvZlyRESE\nSXnJkiW4ERZ7Zu/s7AypVCoMKktLS0NsrOm3e2xsLFJTUwGYjri/+irQWH7wwQeRmJiIUaNGQaFQ\nCM+Pk5KSMHXqVAQHB8Pb21vYXiQSdVhvampqu1gef/xxqNXqdvtcHYfxveTkZOTm5kIul+Ohhx4S\nBrR1dCzG8qJFi1BdXY3BgwdDoVBAq9VCLpdDqVRi0KBBmD59OkaMGNFl26WlpZ3ePbg6di8vL6Sk\npODJJ5+EXC7HsGHDOhzo11GuRCIRpk+fjtzcXMhkMmzevBmBgYHt2oqMjMRTTz2FsLAwyGQyxMXF\nCSd6R44cQVhYWIexMsYYsw6L/updSkoKKioqTK7m2c1bsGAB4uPjhef69mzhwoUICQlpd3LFv3rH\nGHMoSfb9q3cW7eybmpowduxY5OTkdHq1zBxXY2MjIiMjO/z/586eMeZQkm7jzp6xzvDJH2PMkXj0\n8EBtTa3F27HLP6rD2LUQES9EWLVqlc1jsJeFc8G5uFVzYY2O/mZwZ8+YjdXU1Ng6BLvBuWjFuWjF\nubh53NkzxhhjDo47e8Zs7PTp07YOwW5wLlpxLlpxLm4eD9BjNqFQKJCfn2/rMBhj7JYil8tx9OjR\n696PO3vGGGPMwfFtfMYYY8zBcWfPGGOMOTju7JlF7dmzB4MGDcKAAQPw4YcfdrjNq6++igEDBkAu\nl5tMz+tousrFiRMnEBYWBjc3N6xcudIGEVpPV7n45ptvIJfLIZPJMHz4cBQUFNggSuvoKhc7duwQ\n5tAICgrC3r17bRCldZjzfQEAhw4dQrdu3cya9vtW1VUutFotevToAaVSCaVSiaVLl167QmLMQgwG\nA/n7+5Ner6empiaSy+VUVFRkss0PP/xA48ePJyKiAwcO0JAhQ2wRqsWZk4vz58/ToUOHKDExkVas\nWGGjSC3PnFzs37+fampqiIgoMzPztv5c1NXVCa8LCgrI39/f2mFahTm5MG43evRomjBhAqWnp9sg\nUsszJxcajYYmTpxodp18Zc8sRqfTISAgABKJBC4uLnjiiSewY8cOk2127tyJp59+GgAwZMgQ1NTU\noKKiwhbhWpQ5ufD29kZwcDBcXFxsFKV1mJOLsLAw9OjRA0DL5+LPP/+0RagWZ04uxGKx8Lqurg5e\nXl7WDtMqzMkFAKxZswZTpkyBt7e3DaK0DnNzQdcxvp47e2YxZ86cwf333y+U77vvPpw5c6bLbRzx\ni92cXNwurjcXn3/+OaKjo60RmtWZm4vt27cjMDAQ48ePR3JysjVDtBpzvy927NiBl156CYDjzrFh\nTi5EIhH2798PuVyO6OhoFBUVXbPObhaJlDGY/4N49dmpI/4AO+Ix3ajryYVGo8GmTZvw888/WzAi\n2zE3FzExMYiJicG+ffswY8YM/PbbbxaOzPrMycXrr7+ODz74QJgM5nqubG8l5uRCpVKhrKwM7u7u\nyMzMRExMDH7//fdOt+fOnlnMvffei7KyMqFcVlaG++6775rb/Pnnn7j33nutFqO1mJOL24W5uSgo\nKMBzzz2HPXv2wNPT05ohWs31fi7Cw8NhMBhQVVWF3r17WyNEqzEnF3l5eXjiiScAAJWVlcjMzISL\niwsmTZpk1VgtzZxceHh4CK/Hjx+P2bNn4+LFi+jVq1fHlf6XgwoYa+vy5cvk5+dHer2eGhsbuxyg\n98svvzjsQCxzcmG0ePFihx6gZ04uSkpKyN/fn3755RcbRWkd5uSiuLiYrly5QkREeXl55OfnZ4tQ\nLe56fkaIiBISEmjbtm1WjNB6zMlFeXm58Lk4ePAg+fr6XrNOvrJnFtOtWzesXbsWUVFRaG5uxrPP\nPovAwEBs2LABAPDCCy8gOjoau3fvRkBAAMRiMb744gsbR20Z5uSivLwcISEhqK2thZOTE1avXo2i\noiLceeedNo7+v2VOLt59911UV1cLz2ZdXFyg0+lsGbZFmJOLbdu24auvvoKLiwvuvPNOpKam2jhq\nyzAnF7cLc3KRnp6O9evXo1u3bnB3d+/yc8F/LpcxxhhzcDwanzHGGHNw3NkzxhhjDo47e8YYY8zB\ncWfPGGOMOTju7BljjDEHx509Y4wx5uC4s2eMMcYcHHf2jDHGmIP7P76cq7gNFWYgAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa57fcec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s2ACNRoP169fjySefxLlz53Dvvfdi/PjxwgmluLgYWq0W69at01s9zSL4MwzDSMWNGzfQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf01478c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print a bar chart of the people who were readmitted race and gender\n",
    "df_raceGroup = copy.deepcopy(df)\n",
    "#replace the 2's with 1's so that the sum works out\n",
    "df_raceGroup.readmitted = df_raceGroup.readmitted.replace(to_replace=2,value=1)\n",
    "df_raceGroup = df_raceGroup.groupby(by=['race','gender'])\n",
    "readmission_rate = df_raceGroup.readmitted.sum() / df_raceGroup.readmitted.count()\n",
    "readmission_rate.plot(kind='barh', stacked=True, color = ['green', 'red'])\n",
    "\n",
    "readmission_counts = pd.crosstab([df['race'],df['gender']], df.readmitted.astype(bool))\n",
    "readmission_counts.plot(kind='bar', stacked=True, color=['green','red'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar chart of ethnic group and gender vs likelihood of readmission\n",
    "\n",
    "This bar chart shows that people of Asian descent were the least likely to be readmitted to the hospital, and that Caucasian females were most likely to be readmitted.  Caucasian males and African Americans of both sexes had about the same probability of readmission.  Hispanics of both sexes were slightly less likely to be readmitted than Caucasians and African Americans.  This shows, perhaps, that diabetic Caucasians are no healthier than diabetic African Americans.  It says nothing about the Caucasian and African Americans in general.\n",
    "\n",
    "The cross-tabulation chart shows the number of patients of each ethnic grouop.  There were very few Asian and Hispanic patients.  Most of the patients were Caucasian, with a smaller percentage of African Americans.\n",
    "\n",
    "And finally we have the plot of gender and glucose serum level vs readmission rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0xaf4d258c>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZNGiQds1z587Rvn37W75HemKydgAWZrJ2ABZmsnYAFmaydgAWZLJ2APWUWV3r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\nBVMIp/4+GPX4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xada6752c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gj30/RMuD/nIHqE+0MeI8egg2Dwk3PhAjk97ib2Zmhvfffx+nT5/G4cOHsXbt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\n79BtoXzs2DH4+vpi2LBhCAoKwtWrVwEAvr6+CA8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      "text/plain": [
       "<matplotlib.figure.Figure at 0xad9974ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print a bar chart of the people who were readmitted race and gender\n",
    "df_serumGroup = copy.deepcopy(df)\n",
    "#replace the 2's with 1's so that the sum works out\n",
    "df_serumGroup.readmitted = df_serumGroup.readmitted.replace(to_replace=2,value=1)\n",
    "df_serumGroup = df_serumGroup.groupby(by=['gender','max_glu_serum'])\n",
    "readmission_rate = df_serumGroup.readmitted.sum() / df_serumGroup.readmitted.count()\n",
    "readmission_rate.plot(kind='barh', stacked=True, color = ['green', 'red'])\n",
    "\n",
    "readmission_counts = pd.crosstab([df['gender'],df['max_glu_serum']], df.readmitted.astype(bool))\n",
    "readmission_counts.plot(kind='bar', stacked=True, color=['green','red'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above shows that for the glucose serum levels collected, those with levels above 300 were most likely to be rehospitalized.  Ladies with serum levels above 300 had an almost 60% chance of returning.  But those with normal levels had about the same chance of returning as those with no measurement at all.\n",
    "\n",
    "However, the bottom chart illustrates that the data for serum levels are relatively sparse.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interesting Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most interesting feature about the data was the counter-intuitive statistic that merely performing the A1C test slightly reduced the readmission rate (about 2%).  \n",
    "\n",
    "The other interesting feature was that the Caucasian and African American statistics on readmission were very similar, at least among diabetic patients.  I had read that African Americans are more likely to have health problems than Caucasians, but this showed that, at least among diabetics, they were rehospitalized at the same rates after their initial hospital visit.\n",
    "\n",
    "The fact that Asians are very noticeably healthier than the other ethnic group was also interesting.  My wife is Asian and diabetic.  Perhaps it is their diet?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Features That Could Be Added"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 45 more attributes in the data set that could be explored. \n",
    "\n",
    "I would be interested in revisiting it and using some of the data reduction techniques discussed in class to consolidate them and see how they affect the readmission rates.  For example, there were many medications that could be correlated against the readmission rate.  And the medical diagnosis codes could be grouped and quantified. Unfortunately I ran out of time on this assignment, and was unable to pursue it further.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exceptional work: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I had to forego this section due to time constraints.  It took me about 22 hours to complete this, and it is 11:00 on Thursday night.  I expect that the next assignment will go better since I have a better understanding of the tools."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This plain text.\n",
    "\n",
    "#Header\n",
    "##Subheader\n",
    "\n",
    "$x=5$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
